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.

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.

AutoScout24 is on a mission to revolutionize how marketing decisions are driven through data and analytics. We are seeking an experienced and motivated Marketing Data Analyst (m/f/d) to take ownership of our marketing analytics framework and help us make data-informed decisions to optimize efficiency and ROI of our multi-channel marketing funnel. This role is perfect for someone who combines strong technical skills with sharp business judgment - and who thrives working close to top management on high-impact, complex analyses. 

Our team treats AI as a core working principle. We actively integrate AI tools into our day-to-day workflows to raise the speed, quality, and depth of our analytics work — and we expect this mindset from everyone who joins us. 

What you will do 

• Design and implement scalable data models and pipelines within our Data Lake, to integrate and manage marketing data across channels. 

• Build and maintain engaging dashboards to monitor demand, user acquisition and marketing efficiency, following best practices in data visualization to deliver clear and actionable insights. 

• Empower marketing teams with self-service analytics by curating reliable data sources and building automated, scalable data solutions that enable independent, data-driven decision-making. 

• Work closely with online, app, and offline marketing teams to deliver insights and recommendations that optimize multi-channel efforts, media planning, budget allocation, and user acquisition. 

• Partner with fellow analysts to evaluate performance across key platforms — including Google Analytics, Adjust, AppsFlyer, Google Ads, Meta, and Criteo — and translate findings into strategy. 

• Lead exploratory analytics projects — including new KPI development, A/B testing, incrementality studies, offline measurement, and attribution modelling — to address evolving business challenges. 

• Contribute technical expertise and business judgment to deep-dive analyses and reports for senior and top management, and lead multi-disciplinary projects end-to-end, managing timelines and stakeholder communication. 

• Leverage AI tools and automation to increase the efficiency, quality, and scale of analytical work — from data preparation and insight generation to reporting and stakeholder communication. 

What you will bring 

• 4+ years of experience in marketing analytics, ideally in online marketplaces, B2C or B2B environments, with a strong grasp of online and app marketing, campaign optimization, and budget allocation. 

• Proficiency in SQL and hands-on experience building data pipelines and ELT workflows using tools like Airflow and dbt, with familiarity across cloud platforms such as BigQuery, Redshift, or Snowflake. 

• Strong experience with BI and dashboarding tools (Tableau, QuickSight, PowerBI, or similar) and a solid understanding of data visualization best practices. 

• Deep understanding of Google Analytics, MMPs (Adjust, AppsFlyer), and major ad platforms (Google, Meta, Criteo), including tracking systems and reporting structures. 

• Proven ability to translate complex data into clear, actionable recommendations for both technical and non-technical audiences, including C-level stakeholders. 

• Outstanding attention to detail and a strong analytical mindset, with a grasp of statistical concepts to deliver effective and pragmatic solutions. 

• A proven track record of proactively and independently leading cross-functional analytics initiatives in a collaborative, multi-level organization. 

• A proactive approach to integrating AI tools into analytical workflows — with genuine curiosity about how AI can improve the speed and quality of data work. 

• Excellent verbal and written English skills. 

Nice to have 

Experience with web/app marketing attribution, budget planning, or forecasting models · Amazon QuickSight · Google Search Console · Google Tag Manager  

Team

Client Bank Solution team is part of the B2B Tribe at Raisin, consisting of 3 teams in total. 

CBS owns the dedicated interfaces used by B2B Partners who operate as their own Service Banks. These Partners offer white-labelled Raisin products through their own frontend applications, integrating them as an extension of their own services for their customers.

The team consists exclusively of backend engineers, who own a legacy ecosystem of financial services and internal tools currently in maintenance mode while building three greenfield services to replace them. You would own the whole lifecycle from design and development through maintenance of the services at runtime. The team is responsible for the 24/7 runtime support of their services.

Your Responsibilities

• You are responsible for the full E2E delivery cycle, from requirements analysis, and effort estimation through design, implementation, QA, delivery, and support.
• You nurture an inspiring environment with open communication and a culture of trust.
• Champion in writing clean, maintainable, and efficient code across the team’s tech stack - mainly Java, but some limited Python usage in Airflow DAGs and Lambda functions - adhering to best practices in software development. Implement new features and maintain existing codebases to ensure high performance and availability.
• You proactively resolve impediments and mitigate risks for your team.
• In case you need to escalate delivery and team risks, you do this in a timely fashion, with a well-articulated solution/mitigation plan.
• You collaborate closely with your team’s Product Manager and Tech Lead.
• You facilitate the team’s work on strategy and planning for the delivery.
• With the Product Manager, you drive the OKR process for your team: communication of objectives, Key result definition, team commitment, and pairing OKRs with individual target agreements for your engineers.
• You recruit, hire, and onboard engineers into the team.
• You develop your engineers’ soft- and technical skill sets according to the company’s goals and needs.
• You craft agreements and individual development plans with every team member, aligned to the team's goals.
• You uncover training opportunities as well as provide coaching for your team.
• You recognize high performance and reward accomplishments; you act on low performance in a timely manner. 
• You establish and keep strong performance standards for your direct reports.

Your Profile

• You have a university degree in Computer Science, Engineering, Information Systems, or related fields or equivalent practical experience.
• You bring at least 2 years of leadership/people management experience in a remote setup and have a strong focus on delivery. 
• You have hands-on experience in technical platform transitions within a company.
• You have strong hands-on experience (min. 8 years) as a Backend Software Engineer and want to stay one of the main contributors to your team.
• You know how to manage software projects to a successful delivery in a collaborative fashion.
• You have up-to-date, in-depth knowledge of the software development lifecycle in modern Frontend and Backend (Java, Springboot) technologies.
• You are comfortable with open code and architecture discussions.
• You bring experience with AWS Services, K8S, and Event-Driven architectures.
• Very good communication skills; Fluency in English; German-language skills are a plus but not mandatory.

Join our mission, join our team – and grow with us!

At Raisin, we care about each other and it is one of our top priorities to foster an open and caring environment in which everyone feels welcome and comfortable. Our culture is strongly driven by our ambitious team, which connects more than 75 different nationalities.

As part of our team, you will benefit from:

• Employee Development Budget of €2,000 and four full training days per year.
• Flexible working hours, home office and 30 vacation days.
• A company pension scheme (Betriebliche Altersvorsorge), which we support with 20%.
• Enjoy more than 50+ different sports with Urban Sports Club: We subsidize your membership with more than €20 per month. 
• Do you miss being in the office? The Deutschland Ticket gets you there, which we subsidize with €25 per month.
• Love cycling? With JobRad, lease the bike of your choice and enjoy tax savings, plus Raisin covers your monthly insurance costs.
• Hungry all the time? Snacks, daily fresh fruit as well as drinks provided at the office.
• You are moving from another country or city to join us? We may support your relocation.

Location: We are flexible! Come and join us either in Hamburg, Berlin or Barcelona. 

Freenow by Lyft empowers smarter mobility decisions helping people to move freely and cities to thrive.

The Freenow marketplace is a real-time, two-sided platform connecting millions of riders and drivers. Its efficiency and scalability are powered by a sophisticated ecosystem of Machine Learning systems spanning key domains such as Pricing, Allocation, and Marketplace Payment&Risk.

In this role, you’ll help advance our MLOps practices, build the services and the infrastructure that scales ML models from research to real-time production and observe how they impact the daily lives of our users and our business.

Be ready to work in a multinational, diverse, highly motivated and collaborative team of passionate developers who strive for excellence and like to have fun doing it.  Are you ready for your next ride?

YOUR DAILY ADVENTURES WILL INCLUDE:

• Developing and maintaining high-performance, distributed systems that serve ML models at scale with low latency
• Collaborating with Data scientists and Software Engineers to solve business-critical problems in real-time.
• Streamlining online serving for classical and deep learning models to accelerate A/B testing cycles and reduce time-to-market
• Enhancing our MLOps platform capabilities to support AI state of the art
• Implementing ML Monitoring and Observability best practices, including automated data drift detection and self-healing system triggers
• Contributing to the evolution of our MLOps stack and practices.

Our Techstack: Python | Airflow | Java | Kubernetes | Kafka | Databricks |AWS & more

TO BE SUCCESSFUL IN THIS ROLE:

• M.S or B.S in Computer Science, Machine Learning, Mathematics or equivalent technical background with related field
• 3+ years of professional experience deploying, scaling and monitoring ML models in production environment 
• Proficiency in Python. Experience in JVM based programming language (Java, Scala, Kotlin) or GO is highly desirable
• Proven experience with large-scale data processing systems and modern data warehousing 
• Strong grasp of classical ML and Fundamental understanding of modern ML algorithms (e.g. DNN, RL, transformers ) and mathematical optimizations e.g LP, Convex Optimization
• Team player, and an avid learner, with a proven capacity to collaborate seamlessly with diverse teams, including those in data, tech, and product, as well as other stakeholders.
• Clear Communication in English. We are a very diverse team with people from all over the world and English is our language of communication, besides Python.
• Passionate about harnessing Artificial Intelligence and have the will to play an essential role in the future of urban transportation.
• Experience with streaming technologies and real-time data processing (e.g., Apache Kafka, Apache Flink, Feature Stores) is a plus.

BENEFITS & PERKS IN A NUTSHELL:

DIVERSITY, EQUITY & INCLUSION:

Freenow is an equal opportunity employer and we consider qualified applicants regardless of race, religion, national origin, gender, gender identity, sexual orientation, disability or age.
We want you to grow and evolve, bring your true self to work. 

ABOUT US:

Freenow by Lyft empowers smarter mobility decisions, helping people to move freely and cities to thrive. Through our multi-mobility app, we feature broad options for everyone across 9 European markets and over 180 cities. Millions of passengers can access services including taxis, private hire vehicles, carsharing, car rental, e-scooters, e-bikes, e-mopeds, and public transport within a single app. 

In July 2025 Freenow was acquired by Lyft, a global mobility platform, which connects riders and drivers for billions of rides across North America and Europe. Together, Freenow and Lyft are creating a more caring and connected world, with transportation for everyone. We are a global, diverse, highly motivated, and collaborative team that strives for excellence and likes to have fun. Ready for your next ride?

Team

The Growth Platform Team at Raisin is a cross-functional team enabling Marketing and product teams to scale growth initiatives across web and mobile. The team owns core growth capabilities such as marketing integrations, user tracking, experimentation, bonus management, and attribution models, providing reliable services and data foundations that power growth across markets.

As part of this team, you’ll work on complex integrations and data-heavy systems, collaborating closely with Marketing and B2C stakeholders. The Growth Platform Team is responsible for the underlying services, data pipelines, and integrations that make growth measurable and scalable. If you enjoy building platform foundations with clear business impact, this team offers a high-impact and highly collaborative environment.

Your Responsibilities

• You are responsible for the full E2E delivery cycle: from requirements analysis, effort estimation, through design, implementation, QA, and delivery.
• You nurture an inspiring environment with open communication and a culture of trust.
• You proactively resolve impediments and mitigate risks for your team.
• In case you need to escalate delivery and team risks, you do this in a timely fashion, with a well-articulated solution/mitigation plan.
• You collaborate closely with your team’s Product Manager and Tech Lead.
• You facilitate the team’s work on strategy and planning for the delivery.
• Together with the Product Manager, you drive the OKR process for your team: communication of objectives, KR definition, team commitment, and pairing OKRs with individual target agreements for your engineers.
• You recruit, hire, and onboard engineers into the team.
• You develop both your engineers’ soft- and technical skill-sets according to the company’s goals and needs.
• You craft agreements and individual development plans with every team member, aligned to the team's goals.
• You uncover training opportunities as well as provide coaching for your team.
• You recognize high performance and reward accomplishments.
• You establish and keep strong performance standards for your direct reports.

Your Profile

• You hold a university degree in Computer Science, Mathematics, Engineering, or a related quantitative field.
• You bring at least 1 year of leadership or people management experience, with a strong interest in growing and supporting others.
• You have hands-on experience with Python, SQL, and Snowflake. Familiarity with frontend technologies is considered a strong plus.
• You have solid experience with data modeling and building ETL/ELT pipelines; experience with tools such as Airflow and dbt is a plus.
• You have worked with data analytics and visualization platforms such as Looker, Tableau, or similar tools.
• You are familiar with Data Engineering best practices, including data quality, monitoring, and observability.
• You have experience driving software projects to successful delivery in a collaborative, cross-functional environment.
• You have a strong understanding of the software development lifecycle in a data engineering context, including pipelines, integrations, and data-driven product workflow.
• You are comfortable participating in open discussions around code, architecture, and technical trade-offs.
• You are fluent in English. German language skills are a plus but not required.

Join our mission, join our team – and grow with us!

At Raisin, we care about each other and it is one of our top priorities to foster an open and caring environment in which everyone feels welcome and comfortable. Our culture is strongly driven by our ambitious team, which connects more than 75 different nationalities.

As part of our team, you will benefit from:

• Employee Development Budget of €2,000 and four full training days per year.
• Flexible working hours, home office and 30 vacation days.
• A company pension scheme (Betriebliche Altersvorsorge), which we support with 20%.
• Enjoy more than 50+ different sports with Urban Sports Club: We subsidize your membership with more than €20 per month. 
• Do you miss being in the office? The Deutschland Ticket gets you there, which we subsidize with €25 per month.
• Love cycling? With JobRad, lease the bike of your choice and enjoy tax savings, plus Raisin covers your monthly insurance costs.
• Hungry all the time? Snacks, daily fresh fruit as well as drinks provided at the office.
• You are moving from another country or city to join us? We may support your relocation.

What is the opportunity?

Intercom’s AI Group is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands.

We are extremely product-focussed. Our team of 50+ ML scientists, ML engineers, designers and researchers works in partnership with other teams across the whole company. We move to production fast, often shipping to beta within weeks of a successful offline test. 

We constantly run experiments and measure the success of our AI features. We use frequentist and Bayesian approaches. We create dashboards to track results. We dive deep into exactly how users are being successful, and have to tease out all sorts of complex user interactions. We have to deal with inherently stochastic AI products, and complex effects.

We are looking for a dedicated Analytics Engineer to join the AI Group to help us with that.

What will I be doing?

• Data Platform Development: Design, build, and manage scalable data pipelines and ETL processes to support a robust, analytics-ready data platform.
• Cross-functional Collaboration: Partner with ai analysts, ml scientists, engineers and business teams to understand data needs and ensure accurate, reliable & ergonomic data solutions.
• Data Strategy & Governance: Lead initiatives in data model development, data quality ownership, warehouse management, and production support for critical workflows.
• Advanced Analytics & Insights: Conducted data analysis and built custom models to support strategic business decisions and performance measurement.
• Automation & Optimization: Streamline data collection and reporting processes to reduce manual effort and improve efficiency.
• Innovation in Data Infrastructure: Created scalable solutions like unified data pipelines and access control systems to meet evolving organizational needs.
• Strategic Influence: Worked with partner teams to align data collection with long-term analytics and feature development goals.

About You

• You write advanced SQL with a preference for well-architected data models, optimized query performance, and clearly documented code
• You’re familiar with the modern data stack. dbt and Snowflake experience are a big plus.
• A growth mindset and eagerness to learn.
• You exhibit great judgment and sharp business and product instincts that allow you to differentiate essential versus nice-to-have and to make good choices about trade-offs
• You practice excellent communication skills, and you tailor explanations of technical concepts to a variety of audiences

Nice to haves

• Exposure to Apache Airflow or other DAG frameworks — we use Airflow to orchestrate and schedule all of our data workflows and transformations
• Worked in Tableau, Looker, or similar visualization/business intelligence platform
• Experience with operational tools and business systems such Google Analytics, Marketo, Salesforce, Segment, or Stripe
• Familiarity with Python

Benefits 

#LI-Hybrid

What is the opportunity?

Intercom’s AI Group is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands.

We are extremely product-focussed. Our team of 50+ ML scientists, ML engineers, designers and researchers works in partnership with other teams across the whole company. We move to production fast, often shipping to beta within weeks of a successful offline test. 

We constantly run experiments and measure the success of our AI features. We use frequentist and Bayesian approaches. We create dashboards to track results. We dive deep into exactly how users are being successful, and have to tease out all sorts of complex user interactions. We have to deal with inherently stochastic AI products, and complex effects.

We are looking for a dedicated Analytics Engineer to join the AI Group to help us with that.

What will I be doing?

• Data Platform Development: Design, build, and manage scalable data pipelines and ETL processes to support a robust, analytics-ready data platform.
• Cross-functional Collaboration: Partner with ai analysts, ml scientists, engineers and business teams to understand data needs and ensure accurate, reliable & ergonomic data solutions.
• Data Strategy & Governance: Lead initiatives in data model development, data quality ownership, warehouse management, and production support for critical workflows.
• Advanced Analytics & Insights: Conducted data analysis and built custom models to support strategic business decisions and performance measurement.
• Automation & Optimization: Streamline data collection and reporting processes to reduce manual effort and improve efficiency.
• Innovation in Data Infrastructure: Created scalable solutions like unified data pipelines and access control systems to meet evolving organizational needs.
• Strategic Influence: Worked with partner teams to align data collection with long-term analytics and feature development goals.

About You

• You write advanced SQL with a preference for well-architected data models, optimized query performance, and clearly documented code
• You’re familiar with the modern data stack. dbt and Snowflake experience are a big plus.
• A growth mindset and eagerness to learn.
• You exhibit great judgment and sharp business and product instincts that allow you to differentiate essential versus nice-to-have and to make good choices about trade-offs
• You practice excellent communication skills, and you tailor explanations of technical concepts to a variety of audiences

Nice to haves

• Exposure to Apache Airflow or other DAG frameworks — we use Airflow to orchestrate and schedule all of our data workflows and transformations
• Worked in Tableau, Looker, or similar visualization/business intelligence platform
• Experience with operational tools and business systems such Google Analytics, Marketo, Salesforce, Segment, or Stripe
• Familiarity with Python

Benefits 

#LI-Hybrid

Senior Analytics Engineer 

Communication has changed for people. It’s changed for businesses, too.

Intercom is the AI Customer Service company on a mission to help businesses provide incredible customer experiences.

Our AI agent Fin, the most advanced customer service AI agent on the market, lets businesses deliver always-on, impeccable customer service and ultimately transform their customer experiences for the better. Fin can also be combined with our Helpdesk to become a complete solution called the Intercom Customer Service Suite, which provides AI enhanced support for the more complex or high touch queries that require a human agent.

Founded in 2011 and trusted by nearly 30,000 global businesses, Intercom is setting the new standard for customer service. Driven by our core values, we push boundaries, build with speed and intensity, and consistently deliver incredible value to our customers.

What is the opportunity?

Intercom’s AI Group is responsible for defining new ML features, researching appropriate algorithms and technologies, and rapidly getting first prototypes in our customers’ hands.

We are extremely product-focussed. Our team of 50+ ML scientists, ML engineers, designers and researchers works in partnership with other teams across the whole company. We move to production fast, often shipping to beta within weeks of a successful offline test. 

We constantly run experiments and measure the success of our AI features. We use frequentist and Bayesian approaches. We create dashboards to track results. We dive deep into exactly how users are being successful, and have to tease out all sorts of complex user interactions. We have to deal with inherently stochastic AI products, and complex effects.

We are looking for a dedicated Analytics Engineer to join the AI Group to help us with that.

What will I be doing?

• Data Platform Development: Design, build, and manage scalable data pipelines and ETL processes to support a robust, analytics-ready data platform.
• Cross-functional Collaboration: Partner with ai analysts, ml scientists, engineers and business teams to understand data needs and ensure accurate, reliable & ergonomic data solutions.
• Data Strategy & Governance: Lead initiatives in data model development, data quality ownership, warehouse management, and production support for critical workflows.
• Advanced Analytics & Insights: Conducted data analysis and built custom models to support strategic business decisions and performance measurement.
• Automation & Optimization: Streamline data collection and reporting processes to reduce manual effort and improve efficiency.
• Innovation in Data Infrastructure: Created scalable solutions like unified data pipelines and access control systems to meet evolving organizational needs.
• Strategic Influence: Worked with partner teams to align data collection with long-term analytics and feature development goals.

About You

• You write advanced SQL with a preference for well-architected data models, optimized query performance, and clearly documented code
• You’re familiar with the modern data stack. dbt and Snowflake experience are a big plus.
• A growth mindset and eagerness to learn.
• You exhibit great judgment and sharp business and product instincts that allow you to differentiate essential versus nice-to-have and to make good choices about trade-offs
• You practice excellent communication skills, and you tailor explanations of technical concepts to a variety of audiences

Nice to haves

• Exposure to Apache Airflow or other DAG frameworks — we use Airflow to orchestrate and schedule all of our data workflows and transformations
• Worked in Tableau, Looker, or similar visualization/business intelligence platform
• Experience with operational tools and business systems such Google Analytics, Marketo, Salesforce, Segment, or Stripe
• Familiarity with Python

#LI-Hybrid

About Atari

Atari is an interactive entertainment company and an iconic gaming industry brand recognized worldwide for its multi-platform games and licensed products. Atari owns and/or manages a portfolio of more than 400 games and franchises, including globally recognized brands such as Asteroids®, Centipede®, Missile Command®, Pong®, and RollerCoaster Tycoon®.

The Atari family of brands includes Digital Eclipse, Nightdive Studios, Infogrames, AtariAge, MobyGames, as well as Coatsink, Early Morning Studios, and Stormteller Games—spanning game development, publishing, and community experiences worldwide.

Atari operates internationally with offices in New York, Paris, Germany, and India.

Overview

Build and own the data pipelines and knowledge systems that power AI automation. You own the schema, the sources, the quality, and the completeness — working closely with the engineering team to ensure the right knowledge is always in the system and working.

What You’ll Do

Data Schema Design and Ownership

• Design and own canonical data schemas that structure knowledge for AI consumption — versioned, documented, and treated as engineering artefacts.
• Evolve schemas as AI system requirements change: assess downstream impact and coordinate with the engineering team before any rollout.
• Keep the knowledge model legible to domain experts, AI engineers, and future team members through clear documentation of schema decisions.

Source Integration and Pipeline Engineering

• Identify and connect to source systems — databases, repositories, APIs, workflow outputs, domain expert inputs — and build reliable, monitored ingestion pipelines for each.
• Build and maintain production-grade data pipelines using pipeline orchestration tooling (such as Apache Airflow, Prefect, or equivalent): extraction, transformation, validation, loading, error handling, and full observability.
• Keep pipelines current as sources change, new knowledge is produced, or schemas evolve — downstream AI systems are never fed stale or broken data.

Data Cleaning, Transformation, and Quality

• Transform raw source material into structured, consistent, agent-consumable knowledge: normalise formats, resolve conflicts, enforce schema conformance, and eliminate ambiguity.
• Own data quality end-to-end: define standards, build validation logic at ingestion using data quality frameworks (such as Great Expectations or equivalent), and maintain quality metrics across the full knowledge estate.
• Trace quality issues to their root — pipeline fault, schema gap, or source problem — and fix there, not downstream.

Data Systems Maintenance and Lifecycle Management

• Maintain all data pipelines and knowledge systems in production: monitor health, manage dependencies, handle source system changes, and ensure nothing degrades silently over time.
• Run periodic data cleanup: identify and remove stale, duplicate, or degraded knowledge assets that would dilute AI system quality if left in place.
• Manage knowledge deprecation cleanly: when data becomes outdated or superseded, retire it from the system without breaking downstream pipelines or AI system behaviour.

AI Knowledge System Partnership

• Work in a closed loop with the engineering team: diagnose knowledge gaps when AI outputs fail, fix at the pipeline or schema level, and validate through the evaluation framework.
• Understand how RAG retrieval and context assembly work well enough to make data design decisions with AI consumption as the primary constraint.
• Proactively surface knowledge gaps by monitoring AI system failures and escalations, and address root causes before they become production issues.
• Extract specialist knowledge from domain experts and convert it into structured, pipeline-managed data assets the AI system can rely on.

Data Infrastructure and Governance

• Select and manage data storage infrastructure appropriate to each knowledge type — relational databases, document stores, and vector databases (such as Pinecone, Weaviate, or pgvector) — with AI consumption as the deciding factor.
• Implement full data lineage and audit trails: origin, transformation history, and change log for every knowledge asset.
• Enforce data versioning and change management across the knowledge estate, with rollback capability preserved at every step.
• Build a data estate that is legible and accessible across the organisation — laying the foundation for intelligence and analytics as the product portfolio grows.

Stakeholder Communication and Collaboration

• Translate between domain and engineering language in both directions — extracting knowledge requirements from non-technical stakeholders and communicating data decisions and tradeoffs back clearly.
• Work with domain experts to understand how data should be structured and what outputs need to look like — then convert that directly into schema and pipeline requirements.
• Keep engineering, product, and domain stakeholders appropriately informed on pipeline health, data quality, and knowledge gaps — right level of detail, right audience.

Requirements & Qualifications

• Proven track record building and owning production data pipelines end-to-end: source integration, transformation, validation, loading, observability, and ongoing maintenance.
• Strong data schema design skills: canonical data modelling, schema evolution management, downstream impact assessment, and decision documentation.
• Experience integrating with diverse source systems — APIs, databases, document repositories, event streams — with reliable, monitored ingestion pipelines.
• Hands-on experience with pipeline orchestration tooling (such as Apache Airflow, Prefect, or equivalent): scheduling, dependency management, failure handling, and pipeline observability.
• Data cleaning and transformation expertise: normalisation, conflict resolution, format standardisation, and quality validation at scale using data transformation tools (such as dbt or equivalent).
• Experience with vector databases (such as Pinecone, Weaviate, or pgvector) and relational or document stores: schema design, indexing, and production data management.
• Working knowledge of how RAG systems and context assembly operate — sufficient to make data design decisions with AI consumption as the primary constraint.
• Experience partnering with AI or ML engineers: translating knowledge requirements into pipeline tasks and closing feedback loops between AI output quality and data quality.
• Ability to interrogate and profile data across sources — querying, inspecting, and validating content using SQL and Python to identify anomalies, gaps, and quality issues before they reach AI systems.
• Cloud platform experience (AWS, Azure, or GCP): deploying and operating data pipelines and storage systems at production reliability standards.

Preferred / Nice-to-Have

• Experience in the gaming industry: game development pipelines, content management, or platform-specific data and metadata requirements.
• Familiarity with game engine data formats, asset pipelines, or platform SDK data structures (Xbox GDK, PlayStation SDK, or similar).

To Apply

Please submit your resume and a brief cover letter outlining your experience and interest in the role. If available, you are also welcome to include a link to your portfolio.

EEO Statement

Atari is an equal opportunity employer and we are committed to providing a workplace free from harassment and discrimination.  We are committed to equal employment regardless of race, religion or lack thereof, color, national origin, gender, sexual orientation, gender identity or expression, age, marital status, medical condition, veteran status, ancestry, disability status, pregnancy, parental status, genetic information, political affiliation, or any other status protected by the laws or regulations in the locations where we operate.

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

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.

Unsere Engineering-Community wächst, und wir suchen einen (Senior) Data Architect (m/f/*) mit Fokus auf Databricks, die/der unser Team in Deutschland verstärkt.

Als (Senior) Data Architect (m/f/*) entwickelst du die strategische Datenarchitektur auf der Databricks Lakehouse Platform. Du gestaltest skalierbare, sichere und leistungsfähige Datenlandschaften und arbeitest eng mit Data Engineers, Data Scientists und Business-Stakeholdern zusammen, um Datenlösungen von der Idee bis zur Umsetzung zu begleiten.

WAS WIR DIR BIETEN

• Flexible Arbeitsmöglichkeiten, einschließlich vollständig Remote oder hybrider Arbeitsmodelle (Bewerber*innen müssen in Deutschland ansässig sein)
• Die Chance, deine Karriere zu beschleunigen und mit herausragenden Kolleg*innen in einer unterstützenden Learning Community über 3 Kontinente hinweg zu arbeiten
• Bringe deine Ideen in unsere einzigartigen Projekte ein und gestalte sie, indem du sie in die Realität umsetzt
• Finde die richtige Balance zwischen Arbeit und Privatleben: entscheide selbst, ob du von zu Hause, im Büro oder hybrid arbeitest
• Jährliche Leistungsbeurteilungen und regelmäßige Feedback-Zyklen, die echten Mehrwert schaffen, indem sie Kolleg*innen über Netzwerke statt über Hierarchien verbinden
• Einen individuellen Entwicklungsplan und professionelle Weiterbildungsmöglichkeiten
• Zugang zu Lernressourcen wie bezahlten Zertifizierungen, unbegrenztem Zugang zu Udemy Business usw.
• Lokale, virtuelle und globale Team-Events, bei denen sich UT-Kolleg*innen weltweit kennenlernen

DEINE AUFGABEN

• Entwerfen einer zukunftssicheren Datenarchitektur auf der Databricks Lakehouse Platform
• Definieren von Standards und Best Practices für Datenmodellierung, Governance, Qualität und Sicherheit
• Beratung von Kunden und internen Teams bei Architekturentscheidungen, einschließlich Integration von Streaming- und Batch-Workloads
• Übersetzen von Business-Anforderungen in technische Architektur-Designs und Blaupausen
• Auswahl und Bewertung geeigneter Tools und Technologien im Databricks- und Cloud-Ökosystem (Azure, AWS, GCP)
• Enge Zusammenarbeit mit Data Engineers, um Architekturvorgaben in produktionsreife Pipelines und Workflows zu überführen
• Unterstützung bei der Kostenoptimierung und Performance-Steuerung von Databricks-Umgebungen
• Mentoring von Engineers und Aufbau einer starken Data-Engineering- und Architektur-Community im Unternehmen

DEIN PROFIL

• Mehrjährige Erfahrung in der Konzeption und Implementierung von Datenarchitekturen auf Databricks (Lakehouse, Spark, Delta Lake, Unity Catalog)
• Tiefes Verständnis von Cloud-Plattformen (Azure, AWS oder GCP) und deren nativen Daten-Services
• Erfahrung mit Datenmodellierung, Datenintegration, Metadatenmanagement und Governance
• Kenntnisse in Programmiersprachen wie Python oder Spark sowie in Infrastructure-as-Code (Terraform) und Orchestrierung (z. B. Databricks Workflows, Airflow)
• Strategische Denkweise kombiniert mit der Fähigkeit, komplexe Anforderungen in klare Architektur-Designs zu übersetzen
• Pro-aktive Persönlichkeit mit starken Kommunikationsfähigkeiten in Deutsch & Englisch

Our Data & Engineering community is growing, and we’re now looking for a Senior Data Consultant (m/f/*) to join our team in Germany, supporting our global growth.

As a Senior Data Consultant, you will combine deep hands-on experience in modern data platforms — particularly Databricks — with a strategic, client-oriented mindset. You’ll advise organizations on how to design, implement, and scale data-driven architectures, while guiding them through every stage of their digital transformation journey.

WHAT WE OFFER

• Flexible work options, including fully remote or hybrid arrangements (candidates must be located in Germany)
• The chance to accelerate your career and work with outstanding colleagues in a collaborative, global learning community across three continents
• Opportunities to shape cutting-edge data projects and see your ideas implemented in real-world solutions
• Balance your work and personal life: choose to work from home, in the office, or in a hybrid setup
• Annual performance reviews and continuous feedback focused on collaboration and growth, not hierarchy
• A personalized development plan and professional advancement opportunities
• Access to learning resources — paid certifications, unlimited Udemy Business, and more
• Local, virtual, and global team events to connect with colleagues worldwide

WHAT YOU’LL DO

• Advise clients on the design and implementation of scalable data architectures using Databricks Lakehouse Platform, with a strong focus on Apache Spark, Delta Lake, and Unity Catalog
• Translate complex business requirements into data strategies, architecture blueprints, and technical roadmaps
• Design, implement, and optimize data pipelines for analytics, machine learning, and business intelligence workloads
• Collaborate with stakeholders — from executives to technical teams — to align data initiatives with business goals
• Ensure data quality, governance, and security best practices within Databricks and related ecosystems
• Support pre-sales and discovery activities by identifying client needs and defining technical approaches
• Mentor junior consultants and contribute to the growth of our Databricks and data consulting expertise
• Stay current with emerging technologies and trends in cloud data platforms and AI-driven analytics

WHAT YOU’LL BRING

• Proven experience in data consulting or architecture, with hands-on expertise in Databricks on Azure, AWS, or GCP
• Deep knowledge of Spark (PySpark/Scala), Delta Lake, and MLflow
• Strong programming skills in Python or Scala, and experience with CI/CD pipelines (GitHub Actions, Azure DevOps, etc.)
• Solid understanding of data modeling, distributed computing, and performance optimization in cloud environments
• Experience advising or working directly with enterprise clients on data modernization or cloud migration projects
• Familiarity with orchestration tools (e.g., Databricks Workflows, Airflow) and infrastructure-as-code (e.g., Terraform)
• Excellent communication and stakeholder management skills, with the ability to translate technical solutions into business value
• Professional proficiency in German and English (C1 level, written and spoken)

Did we pique your interest, or do you have any questions?
We’d love to hear from you — contact us at recruiting@ultratendency.com