Role & Team

As a Data Engineering Manager, you will lead across several cross-functional teams, managing up to ~10 data engineers and GIS specialists. You will support teams working on data processing and pipelines to enable ML-powered product capabilities.

Our product teams are cross functional and will typically include product managers, designers, engineers and subject matter experts relating to the team’s domain.

You will be accountable for the technical direction and delivery outcomes of your area. You will challenge your teams to pursue ambitious goals while providing a high level of support — growing engineering talent and fostering a highly collaborative, team-based environment where people can do great work.

Your primary focus will be on building high-performing teams and driving results through others. You will not be expected to spend significant time doing hands-on coding; however, you will be expected to dive deep technically when required and leverage your experience to support strong technical decision-making.

Time zone requirement: Europe (GMT/WET, CET, EET) and Eastern North America (NST, AST, EST)

What You'll Do

• Enable our data engineers to become highly productive members of their cross-functional teams, empowering them to ship high-quality, reliable data platforms and pipelines on a daily basis.
• Grow the teams by attracting great talent from your network.
• Foster an inclusive and caring culture where everyone can do their best work.
• Provide regular 1:1 coaching and feedback to tap into the potential of all your team members and help them thrive and reach their aspirations.
• Be a strategic partner to product managers, machine learning engineers, and other stakeholders within your domains and ensure that we are making the correct data architecture and platform decisions that balance short and long term goals.
• Ensure suitability of the data architecture, pipelines, and data models within your domains to enable longer term product, analytics, and machine learning goals.
• Work closely with the other Product & Engineering leaders on strategy, technology and people, including improving our data engineering practices and introducing capabilities that can accelerate data access, reliability, and unlock new product and data-driven opportunities

Skills & Experience

• Experience working in a high growth scaleup environment
• Passionate about climate
• Product minded and able to demonstrate significant impact you have had on a business through the application of data platforms and data-driven systems
• 2+ years experience leading and managing data engineers across multiple teams
• Experience working with the following (or similar) technologies: Python, SQL, data pipeline orchestration tools (e.g., Airflow, Dagster), data warehouses/lakehouses, Postgres DB, data pipelines
• Ability to unblock your team by contributing at a senior engineering level (e.g., reviewing data models, debugging pipelines, guiding architecture). How hands-on you'll be in this role will depend on team size and maturity
• Comfortable working in a build it, run it environment (GCP, cloud run, grafana, k8s) - or similar, with an understanding of data reliability, observability, and pipeline operations
• Strong leadership skills with the ability to mentor and coach engineers at all levels
• Excellent communication skills and ability to collaborate effectively in a cross-functional team environment
• Passion for learning and staying updated with evolving technologies and industry trends, particularly in data engineering and data infrastructure

Nice To Have

• Experience working with maps/imagery/geospatial domains  

Note: We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description.

What We Offer

• Competitive, location-specific compensation and benefits 
• Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around
• Home office stipend, coworking and ongoing education budgets 
• A company culture that genuinely embodies each of our core values
• To be part of truly mission-driven work that reduces wildfires, protects earth’s natural resources and helps solve our climate crisis

The role

We’re looking for a Software Engineer with strong C++ expertise to join the team building and operating Nebius Data Platform — a distributed storage and a processing platform that acts as the company’s “source of truth” and the backbone of many internal (and some external) products.

Nebius Data Platform is a single multi-tenant ecosystem based on YTsaurus — instead of running separate HDFS/Kafka/HBase-style systems, we provide storage, compute, and analytics capabilities inside one platform.

Built on top of the open-source YTsaurus ecosystem, we run and extend our own Nebius distribution and develop significant in-house functionality (core and platform-level). We can design, implement, and roll out features end-to-end on our clusters without waiting for upstream approvals and contribute upstream when it makes sense.

At scale today, this includes~500 servers, ~20k CPU cores and ~10 PB of compressed data in our largest production cluster, supporting workloads ranging from business-critical pipelines and financial transactions to large-scale ML/LLM training datasets and compute.

What’s inside the platform

You’ll work on a system that includes (and ties together):

• Distributed Storage (Cypress): transactional semantics, tiered storage, erasure coding, replication, and strong reliability expectations.
• Compute & ETL: a cluster-wide job scheduler (tens of thousands of cores), MapReduce, YQL for SQL-like data processing, and SPYT (Spark over YTsaurus) for modern data engineering.
• Interactive analytics (CHYT): ClickHouse® instances spun up directly on compute nodes for fast SQL over data in-place.
• Dynamic Tables: low-latency NoSQL KV with distributed ACID transactions for OLTP-style workloads and feature stores.
• Orchestracto: workflow orchestration deeply integrated with the platform (Airflow-like, but platform-native).

What you’ll do

We’re looking for engineers who combine strong systems skills with product sense: understanding who uses the platform, why certain capabilities matter, and making pragmatic trade-offs to maximize impact. On our team, engineering work is expected to be connected to real users and outcomes — you’ll regularly align with internal stakeholders, clarify requirements, and help drive prioritization.

In this role, you will:

• Design and implement new functionality in YTsaurus core (C++) with production reliability in mind.
• Build and evolve platform-level capabilities: platform architecture and operating model—multi-cluster growth, shared primitives, and a consistent experience that scales with new teams and use cases.
• Improve end-to-end platform experience for internal (and external-facing) users: APIs, guardrails, debugging workflows, and automation.
• Own production quality: incident response / on-call rotation, root cause analysis, and turning learnings into durable fixes.

Example projects

• Roll out sharded YTsaurus masters (incl. Kubernetes operator support) and build automatic balancing of metadata across master cells (consensus groups) to remove control-plane bottlenecks and unlock 10–100x cluster growth.
• Make CHYT interactive SQL faster and more predictable at high load via performance work like data-skipping / min-max-style indexes and improved execution introspection.
• Turn Orchestracto into a platform product by defining the building blocks, developer experience, and governance for how teams create and share workflows.
• Scale and harden Parquet-on-S3 for native YTsaurus workloads by tackling replication/movement, consistent lifecycle semantics, and master-server metadata optimizations for performance and reliability.
• Design and ship complete, trustworthy audit trails for data changes (who/what/when) across heterogeneous storage and compute paths.

Tech stack

• Core: modern C++ (C++20, async + multithreaded primitives)
• Services & tooling: Go and Python (microservices, utilities, integration tests)

What we expect

• 5+ years of software engineering experience.
• Strong C++ skills (you’ll write core code).
• Working knowledge of Python and/or Go (you don’t have to be expert, but should be comfortable navigating them).
• Experience developing and/or operating high-load, distributed services.
• Production mindset: ability to use SSH, read logs/metrics/traces, and debug distributed systems behavior.
• Solid CS fundamentals: algorithms, data structures, concurrency basics.

Nice to have

• Experience with Big Data systems (YTsaurus/Hadoop/Spark/ClickHouse/Kafka-like ecosystems).
• Experience with multi-tenant platforms, schedulers, resource isolation, quotas, and reliability engineering.
• Strong performance engineering skills (profiling, lock contention, latency/throughput tradeoffs).

We conduct coding interviews as part of the process.

Responsibilities:

• Design and develop mechanical components and assemblies for server and racks hardware
• Create 3D models, detailed drawings, and specifications using CAD tools
• Collaborate with electrical, systems, and manufacturing engineers for product integration (placement of PCBs, power supplies, cooling systems) 
• Perform thermal and structural analysis to ensure system performance and reliability (if required) 
• Support prototyping, testing, and validation of designs 
• Work with suppliers and manufacturing partners to ensure design for manufacturability (DFM) and cost efficiency 
• Identify and resolve design issues throughout the product lifecycle 

Requirements:

• Bachelor’s or Master’s degree in Mechanical Engineering or related field 
• 3+ years of experience in mechanical design, preferably in electronics or server hardware 
• Proficiency with CAD software (e.g., SolidWorks, Creo, or similar) 
• Strong understanding of thermal management, materials, and manufacturing processes 
• Experience with sheet metal design, plastics, and/or machining 
• Knowledge of data center or server systems is a plus 
• Strong problem-solving skills and attention to details 
• Ability to work effectively in a cross-functional team environment

Nice to have:

• Direct experience designing server, storage, or networking hardware 
• Expertise in thermal design, including airflow optimization, heat sink design, and liquid cooling systems
• Familiarity with data center standards and ecosystems (e.g., rack layouts, cable management, serviceability requirements)
• Experience with simulation tools (CFD/FEA) 
• Familiarity with materials selection for performance, cost, and sustainability considerations 
• Experience with rapid prototyping techniques (3D printing, CNC, sheet metal fabrication)

The role 

The Data Engineering team is responsible for building and maintaining robust data infrastructure that powers analytics, and business intelligence across Nebius. We design and implement scalable data pipelines, optimize data storage and processing, and enable data-driven decision making across the organization. The team works closely with product teams and business stakeholders to ensure alignment with company goals.We are looking for a Data Engineer to design, build, and maintain our data infrastructure and pipelines. You will work on processing large-scale datasets, optimizing data workflows, and enabling analytics capabilities that support our rapidly growing cloud platform.

Your responsibilities: 

• Design, develop, and maintain scalable data pipelines.
• Build and optimize data infrastructure.
• Implement data quality monitoring and validation frameworks.
• Optimize data storage, processing, and query performance for large-scale datasets.
• Design and implement data models for analytics and reporting use cases.
• Develop tools and automation to improve data engineering workflows and productivity.
• Ensure data governance, security, and compliance standards are met.
• Participate in on-call rotation to support production data systems.

Must-haves: 

• 3+ years of experience in data engineering or related roles.
• Experience building and maintaining data pipelines using orchestration tools (e.g., Airflow, Prefect, Dagster).
• Strong proficiency in SQL and solid programming skills in Python.
• Experience with distributed data processing frameworks (e.g., Apache Spark, or similar).
• Knowledge of data modeling principles and best practices.
• Understanding of data architectures and storage systems.

Nice-to-haves: 

• Experience with real-time data streaming platforms.
• Familiarity with Infrastructure as Code tools (Terraform, etc).
• Experience with containerization (Docker) and Kubernetes.
• Knowledge of data governance and privacy frameworks (GDPR, SOC2).
• Knowledge of data quality and observability tools (Great Expectations, etc.).

We conduct coding interviews as part of the process.

Role & Team

As a Senior Engineer, you will have a direct impact on the development of core geospatial products and internal data-processing and annotation tools at Overstory. 

Your breadth of skills will allow you to set a technical vision for the development of the web applications, ETL pipelines, and backend services owned by these teams.

You’ll partner with product managers, external resources, and your team members to execute this vision.

Time zone requirement: Europe (GMT/WET, CET, EET) and North America (NST, AST, EST, CST, MST, PST)

What You’ll Do

• Explore best practices to combine coding agents with software craftsmanship and teach other engineers these practices
• Optimize the orchestration of several object detection and image alignment models
• Design and maintain robust data and feature pipelines for millions of geospatial entities
• Contribute to the technical vision for the development of our internal annotation and QA web applications
• Work closely with your Product Manager to discover and propose new products to solve use cases such as anticipating the impact of an outage at a particular locatio

Skills & Experience

• 5+ years of experience as a senior software engineer 
• Skilled in designing ETL pipelines with near-real-time orchestration, in Python.
• Strong background building and maintaining scalable web-applications with React, FastAPI, PostgreSQL
• Experience with XP practices such as TDD or trunk based development. The team would be new to these practices
• Strong communication skills and ability to explain complex technical concepts to various audience

Nice To Have

• Experience with the Geospatial ecosystem such as PostGIS, Geopandas or Deck.GL
• Experience with Dagster, Airflow or similar orchestration frameworks
• Experience with building web-applications tailored for repeatable human or agentic workflows
• Experience working in B2B, in particular with electrical utilities

Note: We believe that all people are capable of great things. We encourage you to apply even if you do not meet all of the requirements that are listed within this job description.

What We Offer

• Competitive, location-specific compensation and benefits 
• Flexible, autonomous and collaborative working environment rooted in trust - we build our work days around our lives, not the other way around
• Home office stipend, coworking and ongoing education budgets 
• A company culture that genuinely embodies each of our core values
• To be part of truly mission-driven work that reduces wildfires, protects earth’s natural resources and helps solve our climate crisis

Reddit has a flexible first workforce! if you happen to live close to our physical office location our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely from the Netherlands.

We are looking for a skilled backend software engineer to join the Product Catalogs team. Our team builds products and infrastructure that enable retail advertisers to succeed on Reddit. On our team, you’d work projects like:

• Catalog System Scaling - In order to support larger and more sophisticated advertisers, we must scale our system to support millions of products per customer. You’ll have the opportunity to perform load testing, identify bottlenecks, and deploy optimizations to make these customers successful.
• Catalog Management - With millions of products, advertisers need tools to manage those products, identify errors, and build campaigns based on subsets of products.
• Product Enhancement - Leveraging AI, LLMs and Reddit organic data to make our products more appealing to users by showcasing products popular with Redditors, enhancing product image backgrounds with GenAI, and much more!

Technologies used on the team include:

• Languages: Python, Scala, Go
• Frameworks: gRPC, Thrift, Baseplate, Kafka
• Datastores: Postgres, BigQuery, Redis, TiDB
• Tools: Kubernetes, Airflow

Responsibilities:

• Develop, maintain, and scale our product catalogs backend
• Contribute to the development of features to make our product easier to use
• Produce robust and sustainable code and demonstrate proficiency in debugging systems beyond your immediate responsibilities.
• Uphold high-quality standards within the team.

Required Qualifications:

• A Bachelor's degree or equivalent experience in a quantitative or computer science-related field (e.g., mathematics, statistics, economics, finance, computer science)
• 4+ years of full-time backend software engineering experience in a scalable computing environment
• Experience in backend development, with a passion for learning and adapting to new technologies that you lacked prior experience with
• Strong communication and collaboration skills, with the ability to work effectively with diverse stakeholders independently

Nice to Have:

• Prior experience in the ads or ecommerce space

If you're a talented software engineer looking for a new challenge, and you're passionate about building scalable systems and great customer experiences, we encourage you to apply for this exciting opportunity!

What We Offer:

• A dynamic, supportive work environment with a diverse team of engineers and cross-functional partners
• Opportunities for professional growth and development, with a focus on continuous learning and skill-building
• A competitive salary and benefits package, with a focus on work-life balance and employee well-being
• Flexible work arrangements, including remote work options, to support your individual needs and preferences
• The chance to work on high-impact projects that drive real results for our users and advertisers

Benefits:

• Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support
• Family Planning Support
• Gender-Affirming Care
• Mental Health & Coaching Benefits
• Private Pension plan with Employer-matching
• 100% employer-sponsored group medical plan  
• Income Replacement Programs
• Flexible Vacation & Paid Volunteer Time Off
• Generous Paid Parental Leave

About the role:

As a fast-growing business, data is the connective tissue of everything we do - from understanding how customers discover us to deciding on which new features should be rolled out. We're looking for a Data Analyst who doesn't just build dashboards, but who asks the harder questions: why are users doing this, and what should we do about it?

You'll sit at the intersection of engineering and commercial decision-making - trusted by leadership to deliver reliable data and clear narratives, and trusted by the team to keep the pipelines humming. This is an early stage opportunity for an analyst who wants to build. If that sounds like the perfect mix for you, we’d love to chat!

What you will do: 

• Drive business performance, through proactive data analysis, sharp reporting, and actionable insights that land with leadership
• Own and evolve our data pipelines, ensuring data is fresh, accurate, and trustworthy when it matters
• Build and maintain dbt models, that serve recurring business needs across Growth, Finance, and Product
• Be the storyteller in the room, presenting findings and translating complexity into clear narratives that move decisions forward
• Keep a close eye on pipeline health, troubleshoot issues proactively, and champion continuous improvement in data quality

What you will need: 

• You communicate data clearly to non-technical stakeholders - including C-level
• Strong SQL skills and working knowledge of dbt (models, tests, documentation)
• Comfortable writing Python scripts for data transformation or pipeline tooling
• Strong intuition around subscription lifecycle & paid acquisition metrics - you've lived in this space before
• Intellectually curious: You aren’t satisfied with “what” happened; you are focused on “why” it happened.
• An ownership mindset - you communicate proactively, take end-to-end responsibility, and thrive in a small, collaborative team

Nice to have: 

• Experience managing ingestion pipelines or working with orchestration tools like Airflow
• Familiarity with data quality or observability tools
• Experience using AI tools to accelerate data work - LLMs for SQL generation, documentation, anomaly detection, or pipeline debugging

We would also like to offer you:

• Lunch: On days you work in the office a warm fresh lunch will be provided for you.
• Up to 15 days Work From Anywhere Benefit.
• 24 days of paid vacation per year + public holidays.
• Travel Allowance, if you live more than 10 KM from the office (in the form of an NS card)
• Company pension plan.
• Company events and overseas trips!

(Please note that for this role you must already be living in the Netherlands)

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

As a Staff Machine Learning Engineer in our Applied ML & Research team, you'll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.

This role blends hands-on technical work with strategic thinking. You’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.

What you’ll you be doing:

• Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
• Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems.
• Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
• Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
• Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
• Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.

We're looking for someone with:

• Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
• 7+ years of industry experience building and deploying ML models at scale.
• Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
• Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
• Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
• Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
• A track record of shipping production-level ML products and maintaining high code quality.
• Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.

Bonus points for:

• Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
• Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
• Exposure to streaming data platforms like Kafka.
• Contributions to open-source ML projects or publications in ML conferences.

RDQ426R402

At Databricks, we are passionate about enabling data teams to solve the world’s toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world’s best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to tackle technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.

As a Product Manager on the Lakeflow Jobs team, you will shape the future of data-aware orchestration on the Databricks Data Intelligence Platform. Jobs is Databricks’ built-in orchestrator, managing hundreds of millions of workloads every week with 99.95% reliability. It powers ETL, AI/ML, BI, and streaming workloads for thousands of customers. Your focus will be on evolving control-flow capabilities, authoring experiences, and observability features that allow customers to productionize workflows with best practices like source control, code promotion, and system observability.

This role requires a deep understanding of orchestration workflows, data pipelines, and service reliability. You will work closely with engineers, designers, field teams, and customers to define the roadmap, drive adoption, and ensure Jobs is the best-in-class orchestration platform for modern data and AI teams.

The impact you’ll have:

• Define strategy & roadmap: Set vision and priorities across orchestration, triggers, authoring, and observability. Translate customer needs into features and integrations that accelerate Lakeflow Jobs adoption.
• Drive metrics & observability: Define success metrics for adoption, engagement, and developer efficiency. Use data to guide roadmap decisions and refine the product experience.
• Collaborate cross-functionally: Partner with global engineering, field engineering, solution architects, and external partners to deliver impactful features and integrations.
• Champion customer needs: Talk to hundreds of customers, understand their journeys, and advocate for features that reduce friction in authoring, monitoring, and orchestrating workloads.
• Own the product lifecycle: Write PRDs, release notes, and documentation, and drive launches that ensure customer and business success.

What we look for:

• 5+ yrs of experience
• Bachelors degree in Computer Science or similar field
• Experience in Product Management (inbound, outbound, cross-functional processes)
• Solid understanding of major orchestration tools (Airflow, Dagster, Prefect etc.)
• Technical skills understanding of data pipelines (Spark, dbt, Lakeflow Pipelines)
• Strong data analysis and operationalization skills (SQL, Python, building operational dashboards)
  
  

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. By automating routine checks and corrections, our tools speed up production, freeing developers to grow, discover, and create.

Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs. The ML Workflows Engineering team is dedicated to removing infrastructure challenges, streamlining machine learning operations (MLOps), and enabling teams to focus on the innovative work that matters most – building impactful ML models and intelligent agents. As part of the team, you'll play a key role in designing tools, automation, and pipelines that make machine learning development seamless and intuitive.

By integrating cutting-edge MLOps practices and engineering excellence, we aim to maximize productivity and remove the complexity of ML infrastructure so that our teams can push the boundaries of what’s possible in AI.

As part of our team, you will:

• Build tools, automation, and workflows to simplify infrastructure-heavy tasks, empowering AI teams to focus on experimentation and solving core challenges.
• Develop robust monitoring, logging, and tracing systems to ensure the performance and reproducibility of ML workflows in production.
• Design, implement, and maintain end-to-end machine learning pipelines to enable the seamless development, training, and deployment of ML models and intelligent agents.
• Work with large-scale distributed systems, including GPU clusters, to support training, fine-tuning, and evaluation of ML models.
• Collaborate with product and development teams to transform high-level goals into concrete, scalable, and maintainable systems.
• Optimize workflows for reproducibility, scalability, and cost-efficiency while keeping ML teams productive and focused on innovation.

We’ll be happy to have you on our team if you have:

• Hands-on experience with modern MLOps tooling, including Kubernetes, Cloud providers (GCP and AWS), and ML orchestration frameworks.
• A solid understanding of the ML lifecycle from idea to the customer-facing application.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A customer-centric mindset – you care about how ML engineers are actually working and can translate their needs into actionable, scalable, and maintainable architectural decisions.
• Experience with modern CI/CD systems, like GitHub Actions or JetBrains TeamCity.
• At least three years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as ZenML, Dagster, and Airflow.
• Developing infrastructure components and services using cluster solutions like Kubernetes.
• The development of Python-based backend services.
• Creating and maintaining ML pipelines, including legacy ones.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.

We’d be especially thrilled if you have experience with: 

• LLM inference frameworks such as vLLM, DeepSpeed, and TensorRT.
• Writing and maintaining Python libraries used by internal (or external) ML engineers.
• A strong theoretical background in NLP and transformer-based approaches.
• Writing code in Java and/or Kotlin.

#LI-HYBRID
#LI-MR1

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Eligible locations:

• USA
• Canada
• UK
• Portugal
• Spain
• France
• Italy
• Germany
• Netherlands
• Poland
• Czech republic
• Israel
• UAE

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Eligible locations:

• USA
• Canada
• UK
• Portugal
• Spain
• France
• Italy
• Germany
• Netherlands
• Poland
• Czech republic
• Israel
• UAE

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

Eligible locations:

• USA
• Canada
• UK
• Portugal
• Spain
• France
• Italy
• Germany
• Netherlands
• Poland
• Czech republic
• Israel
• UAE

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.

Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.

About the role

As a Senior Software Engineer specializing in Python and the Data Ecosystem, you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.

We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:

• Orchestration Platforms: Apache Airflow, Dagster, Prefect
• Transformation Tools: dbt, SQLMesh
• AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications

ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do

• Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
• Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards
• Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
• Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
• Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
• Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

About you

• 7+ years of software development experience, ideally with hands-on time as a Data Engineer, Data Scientist, or ML Engineer
• Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)
• Solid experience with the Python data ecosystem: Pandas, NumPy, Pydantic, and related libraries
• Prior contributions to, or deep practical experience with, popular data orchestration tools (Airflow, Dagster, or Prefect)
• Hands-on experience with AI/ML in data engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling in production, not just experimentation
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases
• Solid experience with concurrent Python: threading, multiprocessing, and async patterns
• Outstanding written and verbal communication skills; comfortable collaborating across engineering functions and with open-source communities

Bonus points for:

• Experience deploying AI/ML models in production, including inference APIs and vector databases
• Prior experience as a Data Engineer or Data Scientist in a product-facing or platform role
• Familiarity with ClickHouse or similar high-performance OLAP platforms
• Familiarity with the JVM ecosystem

At JetBrains, code is our passion. Ever since we started, back in 2000, we’ve been striving to make the strongest, most effective developer tools on earth. Today, AI-powered assistance and agents are becoming a core part of how developers work in our IDEs.

We’re building multi-step coding agents that can understand large codebases, plan changes, call tools, and iterate with the user. As a Research Engineer in the Agentic Models team, you’ll be responsible for the models, training loops, and evaluation pipelines that power these agents.

You’ll work at the intersection of SFT and RL-style post-training, and product-driven evaluation, using our distributed GPU and MapReduce clusters to ship models into JetBrains products.

As part of our team, you will:

• Design, implement, and maintain SFT and RL post-training pipelines for multi-step coding agents.
• Train and adapt LLMs for agent workflows, including planning, tool use, and multi-step interactions inside JetBrains IDEs.
• Build and develop evaluation and simulation environments where coding agents can act, be measured, and compared on realistic developer tasks.
• Design evaluation frameworks and metrics for agent behavior, analyze traces and logs, and close the loop from evaluation back into training, data, and reward design.
• Analyze training and evaluation results to propose and implement improvements to model architectures, training recipes, and datasets.
• Work with large-scale infrastructure, including distributed training on GPU clusters and large MapReduce-style data processing for pre-training and fine-tuning datasets.
• Collaborate closely with research, product, and infrastructure teams to turn high-level product visions into concrete models, experiments, and shipped features. 

We’ll be happy to bring you on board if you have:

• Extensive hands-on experience training LLMs (pre-training, fine-tuning, or post-training) in a research or production setting.
• Deep expertise in modern deep learning frameworks such as PyTorch, and specialized LLM training stacks (e.g. Megatron, NeMo, verl, or similar).
• Strong theoretical and practical understanding of LLM fundamentals: architectures, tokenization, data pipelines, batching, mixed precision, distributed training, and debugging unstable runs.
• The ability to own projects end to end, starting from a high-level problem or product pain point and overseeing it through the design, experimentation, implementation, and iteration phases.
• A product-aware mindset – you care about how developers actually use agents and can translate product needs and failure modes into modeling and evaluation work.
• At least 3 years of Python experience writing clean, maintainable code in modern ML codebases.

Our ideal candidate would have experience with:

• ML orchestrators and workflow tools such as Kubeflow, Dagster, Airflow, ZenML, and/or job schedulers like Kubernetes or SLURM.
• Large-scale data and training pipelines, e.g. MapReduce-style clusters, multi-node GPU training, or workloads on the order of 1M+ CPU/GPU hours.
• Designing and maintaining evaluation pipelines for LLMs or agents, including metrics, dashboards, experiment tracking, and automated regression checks.
• AI agent development, such as tool-using agents, planners, or multi-step coding workflows, and familiarity with agentic frameworks or patterns.
• Experiment tracking and observability using tools like Weights & Biases, MLflow, Langfuse, or similar.
• Inference optimization and serving optimized models in production.

#LI-KP1

The Connectors team is the bridge between ClickHouse and the entire data ecosystem. We build and maintain the connectors that make ClickHouse accessible to millions of developers and data practitioners worldwide. Our work spans from low-level database integrations to high-level data visualization plugins, ensuring that ClickHouse seamlessly integrates with the tools our users already know and love.

We're responsible for the official data connectors (Kafka, dbt, Spark, Flink, Beam, Fivetran), and integrations with leading visualization platforms (Grafana, Tableau, Power BI, Metabase).

What will you do?

As a Senior Software Engineer specializing in JVM-based frameworks, you'll serve as a core contributor, owning and maintaining critical parts of ClickHouse's Data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience, where you'll craft tools that enable Data Engineers to harness ClickHouse's incredible speed and scale.

You'll own the full lifecycle of data framework integrations - from the core database driver that handles billions of records per second, to SDKs and connectors that make ClickHouse feel native in JVM-based applications. This isn't just about writing code; you're building the foundation that thousands of Data engineers rely on for their most critical data workloads.

Your work will directly impact how companies process massive datasets, from real-time analytics platforms ingesting millions of events per second to observability systems monitoring global infrastructure. You'll collaborate closely with the open-source community, internal teams, and enterprise users to ensure our JVM integrations set the standard for performance, reliability, and developer experience.

About you:

• 6+ years of software development experience focusing on building and delivering high-quality, data-intensive solutions.
• Proven experience with the internals of at least one of the following technologies: Apache Spark, Apache Flink, Kafka Connect, or Apache Beam.
• Experience developing or extending connectors, sinks, or sources for at least one big data processing framework such as Apache Spark, Flink, Beam, or Kafka Connect.
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases.

A track record of building scalable data integration systems (beyond simple ETL jobs)

• Strong proficiency in Java and the JVM ecosystem, including deep knowledge of memory management, garbage collection tuning, and performance profiling.
• Solid experience with concurrent programming in Java, including threads, executors, and reactive or asynchronous patterns.
• Outstanding written and verbal communication skills to collaborate effectively within the team and across engineering functions.
• Understanding of JDBC, network protocols (TCP/IP, HTTP), and techniques for optimizing data throughput over the wire.
• Passion for open-source development.

Bonus points for:

• Prior contributions to open-source projects: actively engaging with the OSS community, advocating for users, and influencing the evolution of the core system through your contributions.
• Familiarity with ClickHouse or similar high-performance data platforms.
• Working knowledge of Python, especially in data engineering contexts (e.g., Pandas, PySpark, Airflow), and ability to contribute to Python tooling when needed.

The Connectors team is the bridge between ClickHouse and the entire data ecosystem. We build and maintain the connectors that make ClickHouse accessible to millions of developers and data practitioners worldwide. Our work spans from low-level database integrations to high-level data visualization plugins, ensuring that ClickHouse seamlessly integrates with the tools our users already know and love.

We're responsible for the official data connectors (Kafka, dbt, Spark, Flink, Beam, Fivetran), and integrations with leading visualization platforms (Grafana, Tableau, Power BI, Metabase).

What will you do?

As a Senior Software Engineer specializing in JVM-based frameworks, you'll serve as a core contributor, owning and maintaining critical parts of ClickHouse's Data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience, where you'll craft tools that enable Data Engineers to harness ClickHouse's incredible speed and scale.

You'll own the full lifecycle of data framework integrations - from the core database driver that handles billions of records per second, to SDKs and connectors that make ClickHouse feel native in JVM-based applications. This isn't just about writing code; you're building the foundation that thousands of Data engineers rely on for their most critical data workloads.

Your work will directly impact how companies process massive datasets, from real-time analytics platforms ingesting millions of events per second to observability systems monitoring global infrastructure. You'll collaborate closely with the open-source community, internal teams, and enterprise users to ensure our JVM integrations set the standard for performance, reliability, and developer experience.

About you:

• 6+ years of software development experience focusing on building and delivering high-quality, data-intensive solutions.
• Proven experience with the internals of at least one of the following technologies: Apache Spark, Apache Flink, Kafka Connect, or Apache Beam.
• Experience developing or extending connectors, sinks, or sources for at least one big data processing framework such as Apache Spark, Flink, Beam, or Kafka Connect.
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases.

A track record of building scalable data integration systems (beyond simple ETL jobs)

• Strong proficiency in Java and the JVM ecosystem, including deep knowledge of memory management, garbage collection tuning, and performance profiling.
• Solid experience with concurrent programming in Java, including threads, executors, and reactive or asynchronous patterns.
• Outstanding written and verbal communication skills to collaborate effectively within the team and across engineering functions.
• Understanding of JDBC, network protocols (TCP/IP, HTTP), and techniques for optimizing data throughput over the wire.
• Passion for open-source development.

Bonus points for:

• Prior contributions to open-source projects: actively engaging with the OSS community, advocating for users, and influencing the evolution of the core system through your contributions.
• Familiarity with ClickHouse or similar high-performance data platforms.
• Working knowledge of Python, especially in data engineering contexts (e.g., Pandas, PySpark, Airflow), and ability to contribute to Python tooling when needed.

The Connectors team is the bridge between ClickHouse and the entire data ecosystem. We build and maintain the connectors that make ClickHouse accessible to millions of developers and data practitioners worldwide. Our work spans from low-level database integrations to high-level data visualization plugins, ensuring that ClickHouse seamlessly integrates with the tools our users already know and love.

We're responsible for the official data connectors (Kafka, dbt, Spark, Flink, Beam, Fivetran), and integrations with leading visualization platforms (Grafana, Tableau, Power BI, Metabase).

What will you do?

As a Senior Software Engineer specializing in JVM-based frameworks, you'll serve as a core contributor, owning and maintaining critical parts of ClickHouse's Data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience, where you'll craft tools that enable Data Engineers to harness ClickHouse's incredible speed and scale.

You'll own the full lifecycle of data framework integrations - from the core database driver that handles billions of records per second, to SDKs and connectors that make ClickHouse feel native in JVM-based applications. This isn't just about writing code; you're building the foundation that thousands of Data engineers rely on for their most critical data workloads.

Your work will directly impact how companies process massive datasets, from real-time analytics platforms ingesting millions of events per second to observability systems monitoring global infrastructure. You'll collaborate closely with the open-source community, internal teams, and enterprise users to ensure our JVM integrations set the standard for performance, reliability, and developer experience.

About you:

• 6+ years of software development experience focusing on building and delivering high-quality, data-intensive solutions.
• Proven experience with the internals of at least one of the following technologies: Apache Spark, Apache Flink, Kafka Connect, or Apache Beam.
• Experience developing or extending connectors, sinks, or sources for at least one big data processing framework such as Apache Spark, Flink, Beam, or Kafka Connect.
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases.

A track record of building scalable data integration systems (beyond simple ETL jobs)

• Strong proficiency in Java and the JVM ecosystem, including deep knowledge of memory management, garbage collection tuning, and performance profiling.
• Solid experience with concurrent programming in Java, including threads, executors, and reactive or asynchronous patterns.
• Outstanding written and verbal communication skills to collaborate effectively within the team and across engineering functions.
• Understanding of JDBC, network protocols (TCP/IP, HTTP), and techniques for optimizing data throughput over the wire.
• Passion for open-source development.

Bonus points for:

• Prior contributions to open-source projects: actively engaging with the OSS community, advocating for users, and influencing the evolution of the core system through your contributions.
• Familiarity with ClickHouse or similar high-performance data platforms.
• Working knowledge of Python, especially in data engineering contexts (e.g., Pandas, PySpark, Airflow), and ability to contribute to Python tooling when needed.

The Connectors team is the bridge between ClickHouse and the entire data ecosystem. We build and maintain the connectors that make ClickHouse accessible to millions of developers and data practitioners worldwide. Our work spans from low-level database integrations to high-level data visualization plugins, ensuring that ClickHouse seamlessly integrates with the tools our users already know and love.

We're responsible for the official data connectors (Kafka, dbt, Spark, Flink, Beam, Fivetran), and integrations with leading visualization platforms (Grafana, Tableau, Power BI, Metabase).

What will you do?

As a Senior Software Engineer specializing in JVM-based frameworks, you'll serve as a core contributor, owning and maintaining critical parts of ClickHouse's Data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience, where you'll craft tools that enable Data Engineers to harness ClickHouse's incredible speed and scale.

You'll own the full lifecycle of data framework integrations - from the core database driver that handles billions of records per second, to SDKs and connectors that make ClickHouse feel native in JVM-based applications. This isn't just about writing code; you're building the foundation that thousands of Data engineers rely on for their most critical data workloads.

Your work will directly impact how companies process massive datasets, from real-time analytics platforms ingesting millions of events per second to observability systems monitoring global infrastructure. You'll collaborate closely with the open-source community, internal teams, and enterprise users to ensure our JVM integrations set the standard for performance, reliability, and developer experience.

About you:

• 6+ years of software development experience focusing on building and delivering high-quality, data-intensive solutions.
• Proven experience with the internals of at least one of the following technologies: Apache Spark, Apache Flink, Kafka Connect, or Apache Beam.
• Experience developing or extending connectors, sinks, or sources for at least one big data processing framework such as Apache Spark, Flink, Beam, or Kafka Connect.
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases.

A track record of building scalable data integration systems (beyond simple ETL jobs)

• Strong proficiency in Java and the JVM ecosystem, including deep knowledge of memory management, garbage collection tuning, and performance profiling.
• Solid experience with concurrent programming in Java, including threads, executors, and reactive or asynchronous patterns.
• Outstanding written and verbal communication skills to collaborate effectively within the team and across engineering functions.
• Understanding of JDBC, network protocols (TCP/IP, HTTP), and techniques for optimizing data throughput over the wire.
• Passion for open-source development.

Bonus points for:

• Prior contributions to open-source projects: actively engaging with the OSS community, advocating for users, and influencing the evolution of the core system through your contributions.
• Familiarity with ClickHouse or similar high-performance data platforms.
• Working knowledge of Python, especially in data engineering contexts (e.g., Pandas, PySpark, Airflow), and ability to contribute to Python tooling when needed.

The Connectors team is the bridge between ClickHouse and the entire data ecosystem. We build and maintain the connectors that make ClickHouse accessible to millions of developers and data practitioners worldwide. Our work spans from low-level database integrations to high-level data visualization plugins, ensuring that ClickHouse seamlessly integrates with the tools our users already know and love.

We're responsible for the official data connectors (Kafka, dbt, Spark, Flink, Beam, Fivetran), and integrations with leading visualization platforms (Grafana, Tableau, Power BI, Metabase).

What will you do?

As a Senior Software Engineer specializing in JVM-based frameworks, you'll serve as a core contributor, owning and maintaining critical parts of ClickHouse's Data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience, where you'll craft tools that enable Data Engineers to harness ClickHouse's incredible speed and scale.

You'll own the full lifecycle of data framework integrations - from the core database driver that handles billions of records per second, to SDKs and connectors that make ClickHouse feel native in JVM-based applications. This isn't just about writing code; you're building the foundation that thousands of Data engineers rely on for their most critical data workloads.

Your work will directly impact how companies process massive datasets, from real-time analytics platforms ingesting millions of events per second to observability systems monitoring global infrastructure. You'll collaborate closely with the open-source community, internal teams, and enterprise users to ensure our JVM integrations set the standard for performance, reliability, and developer experience.

About you:

• 6+ years of software development experience focusing on building and delivering high-quality, data-intensive solutions.
• Proven experience with the internals of at least one of the following technologies: Apache Spark, Apache Flink, Kafka Connect, or Apache Beam.
• Experience developing or extending connectors, sinks, or sources for at least one big data processing framework such as Apache Spark, Flink, Beam, or Kafka Connect.
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases.

A track record of building scalable data integration systems (beyond simple ETL jobs)

• Strong proficiency in Java and the JVM ecosystem, including deep knowledge of memory management, garbage collection tuning, and performance profiling.
• Solid experience with concurrent programming in Java, including threads, executors, and reactive or asynchronous patterns.
• Outstanding written and verbal communication skills to collaborate effectively within the team and across engineering functions.
• Understanding of JDBC, network protocols (TCP/IP, HTTP), and techniques for optimizing data throughput over the wire.
• Passion for open-source development.

Bonus points for:

• Prior contributions to open-source projects: actively engaging with the OSS community, advocating for users, and influencing the evolution of the core system through your contributions.
• Familiarity with ClickHouse or similar high-performance data platforms.
• Working knowledge of Python, especially in data engineering contexts (e.g., Pandas, PySpark, Airflow), and ability to contribute to Python tooling when needed.

The Connectors team is the bridge between ClickHouse and the entire data ecosystem. We build and maintain the connectors that make ClickHouse accessible to millions of developers and data practitioners worldwide. Our work spans from low-level database integrations to high-level data visualization plugins, ensuring that ClickHouse seamlessly integrates with the tools our users already know and love.

We're responsible for the official data connectors (Kafka, dbt, Spark, Flink, Beam, Fivetran), and integrations with leading visualization platforms (Grafana, Tableau, Power BI, Metabase).

What will you do?

As a Senior Software Engineer specializing in JVM-based frameworks, you'll serve as a core contributor, owning and maintaining critical parts of ClickHouse's Data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience, where you'll craft tools that enable Data Engineers to harness ClickHouse's incredible speed and scale.

You'll own the full lifecycle of data framework integrations - from the core database driver that handles billions of records per second, to SDKs and connectors that make ClickHouse feel native in JVM-based applications. This isn't just about writing code; you're building the foundation that thousands of Data engineers rely on for their most critical data workloads.

Your work will directly impact how companies process massive datasets, from real-time analytics platforms ingesting millions of events per second to observability systems monitoring global infrastructure. You'll collaborate closely with the open-source community, internal teams, and enterprise users to ensure our JVM integrations set the standard for performance, reliability, and developer experience.

About you:

• 6+ years of software development experience focusing on building and delivering high-quality, data-intensive solutions.
• Proven experience with the internals of at least one of the following technologies: Apache Spark, Apache Flink, Kafka Connect, or Apache Beam.
• Experience developing or extending connectors, sinks, or sources for at least one big data processing framework such as Apache Spark, Flink, Beam, or Kafka Connect.
• Strong understanding of database fundamentals: SQL, data modeling, query optimization, and familiarity with OLAP/analytical databases.

A track record of building scalable data integration systems (beyond simple ETL jobs)

• Strong proficiency in Java and the JVM ecosystem, including deep knowledge of memory management, garbage collection tuning, and performance profiling.
• Solid experience with concurrent programming in Java, including threads, executors, and reactive or asynchronous patterns.
• Outstanding written and verbal communication skills to collaborate effectively within the team and across engineering functions.
• Understanding of JDBC, network protocols (TCP/IP, HTTP), and techniques for optimizing data throughput over the wire.
• Passion for open-source development.

Bonus points for:

• Prior contributions to open-source projects: actively engaging with the OSS community, advocating for users, and influencing the evolution of the core system through your contributions.
• Familiarity with ClickHouse or similar high-performance data platforms.
• Working knowledge of Python, especially in data engineering contexts (e.g., Pandas, PySpark, Airflow), and ability to contribute to Python tooling when needed.