About Emplifi

Emplifi is a leading AI-powered social media marketing and customer experience platform, empowering brands to deliver meaningful, connected experiences across digital channels. Recognized as a Leader by renowned analysts and celebrated as a customer favorite, Emplifi provides innovative, data-driven insights and AI-powered tools to help brands optimize social media performance, elevate their influencer marketing strategies, and deliver impactful customer engagement across marketing, commerce, and care.

About the Reporting and Analytics Team

We're a group of enthusiastic business analysts, data analysts, engineers, and Salesforce developers. We also have a team consisting of developers taking care of our SaaS toolsets. People matter the most. We try to create a culture where people feel productive and creative, can learn and grow, and every voice is heard. 

Projects run on agile methodology, and we tackle interesting problems from anomaly detection, to optimal product setup, retention prediction to analyzing macro market trends using gigabytes of data. Our stack consists of Python, SQL, Tableau, Apache Spark, AWS S3, and PostgreSQL. We also manage Salesforce (both marketing and sales processes).  While code quality matters, what defines us is our ability to combine strong technical solutions with a sharp business mindset.

About the Role

At Emplifi, data is more than numbers—it’s insight, innovation, and impact. As our Data Analyst, you’ll be at the forefront of shaping business and product decisions across the organization. We are looking for someone with natural curiosity, strong independence, and a deep sense of accountability—someone who thrives on discovering the "why" behind the data and who is motivated to deliver meaningful outcomes. You’ll uncover trends in the social media landscape that feed into global publications, deliver actionable insights that power our strategy, and support client success through fast, accurate data exports.

In this dynamic role, you'll also support Sales enablement and pipeline building, while mastering powerful technologies like Python, Databricks, PySpark, S3, and Airflow. You’ll work with terabytes of data in our evolving Data Lake, all within a collaborative and flexible Agile (SCRUM) environment where experimentation is encouraged and learning never stops.

What you'll Do Here

On-Demand Insights

• Support cross-functional stakeholders—including Management, Marketing, Sales, and Product—with timely, high-impact analyses
• Extract insights from large datasets (like TikTok usage) to inform business strategies and delight clients through ad-hoc data exports

Autonomous Insight Generation

• Work independently to define questions, mine data, perform analyses, and present actionable recommendations
• Take initiative to identify new opportunities where data can drive value

Automated Reporting Pipelines

• Build robust, automated ETL processes for recurring reports and dashboards
• Deliver standardized, visually compelling data products that are reliable and ready to use

Go Beyond Analytics

• Tackle complex challenges that blend analytics and data science - like improving product stickiness or identifying risk patterns
• Use tools like ChatGPT and LLMs to enrich data and streamline analysis for internal teams

What You’ll Bring To Us

Must Have Skills

• Advanced skills in Python, especially with libraries like Pandas and NumPy for report automation
• Proficiency in SQL and confident navigating large datasets
• Solid foundation in statistics, including descriptive and inferential techniques (e.g., bootstrapping)
• Fluency in English (spoken & written), with the ability to collaborate across teams and communicate technical insights to non-technical audiences
• A proactive, solution-oriented mindset - you seek out automation opportunities and innovate with emerging tools
• Ability to manage priorities and meet deadlines in a fast-paced Agile environment
• Strong sense of ownership and accountability - you own your outcomes

Your Work Style

• You actively participate in Agile ceremonies and contribute to our SCRUM culture
• You use JIRA for tracking, GIT for versioning, and follow clean, consistent code practices
• You help set the standard for data quality and scalability
• You choose the right tools and tech for each data challenge and are accountable for your deliverables

Additional Tools

• Experience with APIs and PySpark is a plus
• Familiarity with basic Machine Learning concepts such as regression, classification, and clustering
• Experience with Git/GitHub for version control
• Willingness to learn tools like Airflow, Databricks, or Grafana

What We Offer

• International and fast-paced environment
• Chance to work with the world’s biggest brands at the CX tech leader
• Agile and open-minded culture, with high levels of trust and flexibility
• Opportunity for professional growth and development 
• Possibility to learn new and cutting-edge technologies, in an environment that encourages new ideas
• Flexible working environment
• Unlimited Paid Time Off
• Sick Days & Community Service Days
• Multisport card
• Maternity and Parental Benefit
• Possibility to work from offices in Prague (Karlín), Brno (Impact Hub), Pilsen (Roudná)
• There’s more as well! Speak with us to find out all the details!

As a Senior ML Engineer you will join our Static AI team which develops state-of-the-art ML models that operate on millions of machines worldwide, making sub-second decisions to stop malware before it runs. We bring together cross-functional skill sets, including data science, ML engineering, and software engineering, to research, develop, ship, and support ML malware detection engines across Windows, Mac, and Linux machines and cloud environments.

What Will You Do?

Primary responsibilities include: 

• Data pipeline architecture: Design, build, and maintain scalable data pipelines to handle millions of data points from internal and external sources
• ML infrastructure: Develop and optimize the infrastructure required for training, evaluating, and deploying ML models at scale
• Cross-functional Collaboration: Work closely with Data Scientists and Software Engineers to translate research into production-ready detection engines
• System optimization: Build and improve analytics systems that learn from a wide variety of data sources to enhance our existing ML models
• Production support: Ensure the reliability and performance of ML malware detection engines across Windows, Mac, Linux, and cloud environments
• "Production experience" - good level of coding, tests, dealing with CI
• You will also be encouraged to write white papers, blogs, and articles or present in meetups / conferences (but only if you wish to)

What Skills and Knowledge Will You Bring?

Ideal candidates will have: 

• Degree in Computer Science or Mathematics, or equivalent experience
• Experience with collecting and building large datasets for training machine learning models
• Producing, deploying and running ML in production environments
• Experience working with Big data technologies, such as Spark/Databricks, Hadoop, and others 
• Ability to work collaboratively to translate business requirements into technical solutions
• Willingness and ability to work in a diverse, globally distributed team
• The ability to work on difficult problems independently
• Strong communication skills. Our team relies on strong communication, since we have a diverse and sometimes exclusive set of skills, yet our end product relies on all team members. Thus, we need our team members to be able to communicate with people outside our team (with a wide range of backgrounds/positions, product/support/engineering, …)
• A fiery determination and a strong team-player mindset - the team's defining trait is an 'all hands on deck' attitude

Experience in some of these areas is an advantage

• Experience in the cyber domain - security products, especially detection systems
• Cloud infrastructure (AWS) / MLOps experience
• Experience working with Infrastructure as code (IaC)
• AI tooling experience - We are an AI-oriented engineering team. We believe in automating the mundane so our developers can focus on high-level architecture and complex problem-solving. We provide the latest enterprise AI tools and encourage a culture of 'working smarter, not harder.

Why SentinelOne?

AI is redefining how the world operates and rewriting the rules of security in real time, and SentinelOne was built for this moment. From day one, we architected an AI-native platform designed to operate at machine speed, not as an add-on to legacy systems but as the foundation itself. If you want to build where innovation and impact move together, this is that place.

We invest in our Sentinels with comprehensive, competitive benefits designed to support you and your family:

• Enjoy flexible hybrid work in Prague (Karlin), Prague-based employees are required to work from the office at least two days per week.
• Stock & Bonuses: Grant of Restricted Stock Units with a 4-year vesting plan, annual performance-based bonuses, and an employee stock purchase plan.
• Time Off & Well-being: Flexible Time Off, on top of the standard 5 weeks vacation, flexible paid sick days, fully paid Short Term Sick/Nursing Leave, 16-week parental leave, grandparent leave, and additional company holidays.
• Insurance & Health: Pension Insurance Contribution, Premium life insurance, Private medical care (for you and +1), and a Global Employee Assistance Program.
• Work Perks: Monthly meal and well-being allowance, high-end MacBook/Windows laptop, work-from-home support, and in-office refreshments.
• Growth & Community: LinkedIn Learning, internal mentoring, educational support, generous referral bonuses, and optional company events (sports, BBQs, charity).

Become a Senior SRE for Bloomreach!

Join the newly form Datacraft team — the team building the next-generation data platform for Bloomreach Engagement. Datacraft owns three interconnected domains:

• Data Warehouses (~60%) — making Bloomreach data first-class in customer DWHs (Snowflake, BigQuery, Databricks). The strategic goal for 2026–27 is to use DWHs to exponentially accelerate data adoption.
• Loomi Analytics Agent (~20%) — evolving Loomi Analytics into an agentic analytics assistant that can explore data across systems, explain insights, and act on them.
• Dashboards & Analytics Stack (~20%) — moving Engagement reporting onto DWH-backed, modern analytics stacks (semantic layers, headless BI tools).

As a Senior SRE, you will be the reliability backbone of this AI-first data team. Your work will directly impact the deployments, pipelines, reliability, and observability of pipelines and services that hundreds of enterprise customers depend on — from data exports into Databricks and BigQuery, to the  AI agent Loomi uses to surface insights.

Datacraft is an AI-first team. We believe code is a commodity and expect every engineer to fluently use coding agents (e.g., Cursor, Claude Code, Copilot, Gemini CLI) as a core part of their daily workflow. The ability to leverage AI tooling to accelerate development, prototyping, and problem-solving is not optional — it's foundational. Working in one of our Central European offices (Bratislava, Praha, Brno) or from home on a full-time basis, you'll become a core part of the Engineering team.

What challenge awaits you?

As a P3 (Senior) SRE at Bloomreach, you are an independent professional — expert in reliability engineering, able to decompose objectives into actionable infrastructure improvements, and lead initiatives end-to-end with minimal day-to-day guidance.

We need you to build and operate an ecosystem where data engineers can safely and efficiently develop, debug, and operate data-intensive jobs and services — spanning Kafka ingest pipelines, Iceberg data lakes, multi-DWH exports, Databricks deployment and orchestration (Airflow / Cloud Composer), and agentic AI workloads.

Your responsibilities

a. Platform reliability & observability

• Build and maintain the reliability ecosystem where engineers can safely develop, debug, and operate DataCraft services running on GCP and Kubernetes (DataProc, Cloud Composer, BigQuery, Snowflake/Databricks connectors).
• Ensure end-to-end observability across the full data platform — from Kafka ingest through GCS/Iceberg staging, Airflow orchestration, to Databricks and BigQuery destinations — enabling the team to catch missing loads, SLA breaches, and data drifts before customers notice, or costs drift.
• Drive scalability so services can scale vertically and horizontally based on operational and telemetric data (OpenTelemetry, Prometheus, Victoria Metrics).
• Maintain team health dashboards and alerting (Grafana, PagerDuty, Sentry).

b. Infrastructure as Code & deployments

• Own and evolve Terraform-based infrastructure for DataCraft services.
• Automate deployments, instance setup, and operational runbooks to eliminate manual/semi-manual steps.
• Maintain CI/CD pipelines (GitLab) with linters, security scans, and code quality checks, AI code reviews, enabling engineers to produce high-quality MRs.

c. Security & compliance

• Help the team fulfill security requirements for ISO and SOC2 audits by enforcing security principles: key distribution, key rotation, authorization & authentication at the service level, data encryption in transit, data isolation, resource limitations, and audit logs.
• Ensure data access controls are properly enforced across multi-DWH environments (BigQuery, Snowflake, Databricks).

d. Incident management & L3 support

• Participate in and drive L3 on-call rotation and incident resolution for DataCraft services.
• Contribute tooling for debugging, troubleshooting, and performance testing of data pipelines and orchestration layers.
• Use telemetry data and distributed tracing to navigate complex, distributed service architectures.

e. Agentic platform reliability

• Ensure reliability and observability of the Loomi Analytics Agent data infrastructure — LLM API gateway performance, MCP server health, and evaluation pipeline availability.
• Monitor and alert on data quality issues that could introduce inconsistencies or hallucinations in Loomi's responses — making the agent's data access patterns reliable and debuggable.

Our tech stack

Languages: Python (primary), Go, SQL Messaging & streaming: Apache Kafka Storage & databases: Databricks, BigQuery, Apache Iceberg, GCS, Mongo, Redis Data processing & orchestration: Apache Spark, DataFlow, Airflow / Cloud Composer Infrastructure: GCP, Kubernetes, Terraform AI / Agentic: LLM APIs, MCP, agent orchestration frameworks Observability: Grafana, Prometheus, Victoria Metrics, PagerDuty, Sentry, OpenTelemetry CI/CD & tooling: GitLab, Jira, Confluence AI coding agents: Cursor, Claude Code

Your qualifications

Professional experience

Impact

• You can articulate how your contributions transformed the way engineers work and fostered a strong SRE/DevOps culture.
• You can demonstrate how impactful reliability work connects to business success and customer outcomes.

Ownership

• You embrace the you build it, you run it principle — you love owning what you ship.
• You are cost-aware: effective vertical and horizontal autoscaling and detailed telemetry insights are how you demonstrate mindfulness of cloud spend.

Systematic approach

• Infrastructure as Code is the only thing that brings stability into chaos
• You design for failure: SLOs, error budgets, and runbooks are first-class artifacts, not afterthoughts.

Data-driven

• You use telemetry and metrics to give engineers actionable feedback on how applications and services behave.
• You can navigate complex data platform architectures using distributed tracing and debugging.

Technical skills

• Solid hands-on experience with GCP (BigQuery, DataProc, Cloud Composer, GCS) and Kubernetes.
• Experience with Python; Go is a strong advantage.
• Familiarity with data pipeline technologies (Kafka, Airflow/Cloud Composer, Spark, Iceberg) — you don't need to write ETL code, but you need to operate it reliably and know when something is wrong.
• Fluent use of AI coding agents (Cursor, Claude Code, Copilot, Gemini CLI, or similar) — you already use these tools daily to accelerate work.
• Comfortable with on-call rotation and 24/7 incident response.
• Remote-first mindset — you know how to be effective in distributed teams.
• You are able to learn and adapt — essential when exploring new tech or navigating our growing codebase.

Strongly preferred

• Experience operating single-DWH environments (Snowflak, Databricks or BigQuery).
• Familiarity with agentic/LLM workloads — API reliability, latency SLOs, trace observability for AI systems.
• Experience with open table formats (Iceberg, Delta Lake) in production environments.
• Exposure to data security and compliance in the context of customer-facing DWH integrations (consent, data retention, PII handling).

Personal qualities

• Ownership & accountability — you take issues from detection through to resolution and follow-up prevention.
• Systematic thinking — you identify root causes, not symptoms, and document your findings so the team learns.
• Collaboration & communication — you explain trade-offs and constraints clearly to both engineers and non-engineers.
• Bias for reliability — operational excellence (SLOs, oncall friendliness, proactive alerting) is not a chore, it's your craft.
• Continuous improvement mindset — you are comfortable iterating, revisiting assumptions, and improving incrementally.
• Comfortable operating remote-first in a distributed team across Central Europe.

Your success story

In 30 days:

• Get to know the DataCraft team, the company, and the most important processes.
• Set up your local and GCP development environment and complete the Engagement engineering onboarding.
• Understand the current state of DataCraft services: pipelines, orchestration, observability gaps, and on-call runbooks.

In 90 days:

• Start contributing to the L3 on-call rotation, handling incidents, troubleshooting, and debugging — which will sharpen your understanding of the platform and surface fresh improvement ideas.
• Deliver your first meaningful reliability improvement: an observability enhancement, a deployment automation, or an SLO definition for a key DataCraft service.

In 180 days:

• Own the reliability posture of at least one DataCraft domain end-to-end — able to independently design, operate, and continuously improve it.
• Drive measurable improvements in MTTR, alert signal-to-noise ratio, or deployment confidence across the team.
• Be a trusted reliability partner in architecture discussions — your input shapes how new DataCraft services are designed for operability from day one.

#LI-KP1

Become a Senior SRE for Bloomreach!

Join the newly form Datacraft team — the team building the next-generation data platform for Bloomreach Engagement. Datacraft owns three interconnected domains:

• Data Warehouses (~60%) — making Bloomreach data first-class in customer DWHs (Snowflake, BigQuery, Databricks). The strategic goal for 2026–27 is to use DWHs to exponentially accelerate data adoption.
• Loomi Analytics Agent (~20%) — evolving Loomi Analytics into an agentic analytics assistant that can explore data across systems, explain insights, and act on them.
• Dashboards & Analytics Stack (~20%) — moving Engagement reporting onto DWH-backed, modern analytics stacks (semantic layers, headless BI tools).

As a Senior SRE, you will be the reliability backbone of this AI-first data team. Your work will directly impact the deployments, pipelines, reliability, and observability of pipelines and services that hundreds of enterprise customers depend on — from data exports into Databricks and BigQuery, to the  AI agent Loomi uses to surface insights.

Datacraft is an AI-first team. We believe code is a commodity and expect every engineer to fluently use coding agents (e.g., Cursor, Claude Code, Copilot, Gemini CLI) as a core part of their daily workflow. The ability to leverage AI tooling to accelerate development, prototyping, and problem-solving is not optional — it's foundational. 

For candidates at the P3 / Senior SRE level, starting monthly compensation begins at 3 800 € gross, with the final offer tailored for each candidate based on their skills and experience. Stock options and a comprehensive benefits package are also included. Working in one of our Central European offices (Bratislava, Praha, Brno) or from home on a full-time basis, you'll become a core part of the Engineering team.

What challenge awaits you?

As a P3 (Senior) SRE at Bloomreach, you are an independent professional — expert in reliability engineering, able to decompose objectives into actionable infrastructure improvements, and lead initiatives end-to-end with minimal day-to-day guidance.

We need you to build and operate an ecosystem where data engineers can safely and efficiently develop, debug, and operate data-intensive jobs and services — spanning Kafka ingest pipelines, Iceberg data lakes, multi-DWH exports, Databricks deployment and orchestration (Airflow / Cloud Composer), and agentic AI workloads.

Your responsibilities

a. Platform reliability & observability

• Build and maintain the reliability ecosystem where engineers can safely develop, debug, and operate DataCraft services running on GCP and Kubernetes (DataProc, Cloud Composer, BigQuery, Snowflake/Databricks connectors).
• Ensure end-to-end observability across the full data platform — from Kafka ingest through GCS/Iceberg staging, Airflow orchestration, to Databricks and BigQuery destinations — enabling the team to catch missing loads, SLA breaches, and data drifts before customers notice, or costs drift.
• Drive scalability so services can scale vertically and horizontally based on operational and telemetric data (OpenTelemetry, Prometheus, Victoria Metrics).
• Maintain team health dashboards and alerting (Grafana, PagerDuty, Sentry).

b. Infrastructure as Code & deployments

• Own and evolve Terraform-based infrastructure for DataCraft services.
• Automate deployments, instance setup, and operational runbooks to eliminate manual/semi-manual steps.
• Maintain CI/CD pipelines (GitLab) with linters, security scans, and code quality checks, AI code reviews, enabling engineers to produce high-quality MRs.

c. Security & compliance

• Help the team fulfill security requirements for ISO and SOC2 audits by enforcing security principles: key distribution, key rotation, authorization & authentication at the service level, data encryption in transit, data isolation, resource limitations, and audit logs.
• Ensure data access controls are properly enforced across multi-DWH environments (BigQuery, Snowflake, Databricks).

d. Incident management & L3 support

• Participate in and drive L3 on-call rotation and incident resolution for DataCraft services.
• Contribute tooling for debugging, troubleshooting, and performance testing of data pipelines and orchestration layers.
• Use telemetry data and distributed tracing to navigate complex, distributed service architectures.

e. Agentic platform reliability

• Ensure reliability and observability of the Loomi Analytics Agent data infrastructure — LLM API gateway performance, MCP server health, and evaluation pipeline availability.
• Monitor and alert on data quality issues that could introduce inconsistencies or hallucinations in Loomi's responses — making the agent's data access patterns reliable and debuggable.

Our tech stack

Languages: Python (primary), Go, SQL Messaging & streaming: Apache Kafka Storage & databases: Databricks, BigQuery, Apache Iceberg, GCS, Mongo, Redis Data processing & orchestration: Apache Spark, DataFlow, Airflow / Cloud Composer Infrastructure: GCP, Kubernetes, Terraform AI / Agentic: LLM APIs, MCP, agent orchestration frameworks Observability: Grafana, Prometheus, Victoria Metrics, PagerDuty, Sentry, OpenTelemetry CI/CD & tooling: GitLab, Jira, Confluence AI coding agents: Cursor, Claude Code

Your qualifications

Professional experience

Impact

• You can articulate how your contributions transformed the way engineers work and fostered a strong SRE/DevOps culture.
• You can demonstrate how impactful reliability work connects to business success and customer outcomes.

Ownership

• You embrace the you build it, you run it principle — you love owning what you ship.
• You are cost-aware: effective vertical and horizontal autoscaling and detailed telemetry insights are how you demonstrate mindfulness of cloud spend.

Systematic approach

• Infrastructure as Code is the only thing that brings stability into chaos
• You design for failure: SLOs, error budgets, and runbooks are first-class artifacts, not afterthoughts.

Data-driven

• You use telemetry and metrics to give engineers actionable feedback on how applications and services behave.
• You can navigate complex data platform architectures using distributed tracing and debugging.

Technical skills

• Solid hands-on experience with GCP (BigQuery, DataProc, Cloud Composer, GCS) and Kubernetes.
• Experience with Python; Go is a strong advantage.
• Familiarity with data pipeline technologies (Kafka, Airflow/Cloud Composer, Spark, Iceberg) — you don't need to write ETL code, but you need to operate it reliably and know when something is wrong.
• Fluent use of AI coding agents (Cursor, Claude Code, Copilot, Gemini CLI, or similar) — you already use these tools daily to accelerate work.
• Comfortable with on-call rotation and 24/7 incident response.
• Remote-first mindset — you know how to be effective in distributed teams.
• You are able to learn and adapt — essential when exploring new tech or navigating our growing codebase.

Strongly preferred

• Experience operating single-DWH environments (Snowflak, Databricks or BigQuery).
• Familiarity with agentic/LLM workloads — API reliability, latency SLOs, trace observability for AI systems.
• Experience with open table formats (Iceberg, Delta Lake) in production environments.
• Exposure to data security and compliance in the context of customer-facing DWH integrations (consent, data retention, PII handling).

Personal qualities

• Ownership & accountability — you take issues from detection through to resolution and follow-up prevention.
• Systematic thinking — you identify root causes, not symptoms, and document your findings so the team learns.
• Collaboration & communication — you explain trade-offs and constraints clearly to both engineers and non-engineers.
• Bias for reliability — operational excellence (SLOs, oncall friendliness, proactive alerting) is not a chore, it's your craft.
• Continuous improvement mindset — you are comfortable iterating, revisiting assumptions, and improving incrementally.
• Comfortable operating remote-first in a distributed team across Central Europe.

Your success story

In 30 days:

• Get to know the DataCraft team, the company, and the most important processes.
• Set up your local and GCP development environment and complete the Engagement engineering onboarding.
• Understand the current state of DataCraft services: pipelines, orchestration, observability gaps, and on-call runbooks.

In 90 days:

• Start contributing to the L3 on-call rotation, handling incidents, troubleshooting, and debugging — which will sharpen your understanding of the platform and surface fresh improvement ideas.
• Deliver your first meaningful reliability improvement: an observability enhancement, a deployment automation, or an SLO definition for a key DataCraft service.

In 180 days:

• Own the reliability posture of at least one DataCraft domain end-to-end — able to independently design, operate, and continuously improve it.
• Drive measurable improvements in MTTR, alert signal-to-noise ratio, or deployment confidence across the team.
• Be a trusted reliability partner in architecture discussions — your input shapes how new DataCraft services are designed for operability from day one.

#LI-KP1

Writing software is your hobby. You want to team up with great people because that’s the best way to grow and learn. You want your work to make a positive impact in the world. You love the idea that your code can help combat global crime. Is this you? Then you will fit well in our team. 

We are building cutting-edge solutions to create a safer world and stop money from ending up in the hands of criminals. Your work will allow our clients to identify and understand criminal, financial, and political risks, updating dynamically as new data becomes available from various sources (sanctions, watchlists, corporate registries, media, …).

As a Junior Software Engineer, you will:

• Collaborate on the design and build distributed data pipelines and the system around our Knowledge Graph to build and improve our next-generation anti-money laundering solution.
• Work with our product manager(s) to understand and contribute to our goals, find a solution to reach them and prioritise accordingly.
• Engage in the full product lifecycle, including ideation, design, and deployment and get code from initial requirements to deployed services in production (with help of our SRE teams).
• Improve scalability, latency and throughput of our pipelines while keeping high standards in systems reliability and resilience.
• Model data and design schemas to accommodate the needs of our Knowledge Graph and the whole data ecosystem.
• Collaborate with squads and functions across the company to ensure the quality and impact of your designed solutions.
• Be responsible for the quality of your code. Write tests, document, and take ownership of the systems that ensure the quality of our code.
• Work with ML engineers to build and deploy new features for AI-powered components.
• Actively learn, adopt best practices, identify space for improvement, contribute to our engineering culture and always strive for making us better.
• Use AI-assisted development tools (like Claude Code or Gemini Code Assist) under guidance to improve your productivity and code quality.

Our Tech Stack:

The data-driven nature of the role and the prominence of Python in the data and ML ecosystem means you will primarily work in Python and engineer data pipelines. Any relevant experience is going to be helpful but we believe that top engineers are able to learn quickly and adapt to new technologies so that we can innovate and react to the fast changing world. 

• Our technology stack is designed to run on public cloud architectures, notably AWS and GCP;
• Python and Kotlin programming languages;
• Spark, Databricks, Google Dataproc, Delta lakes for stream and big data processing;
• Relational (Postgres), large scale NoSQL and graph databases and other distributed or cloud-based object stores;
• Event-sourced model powered by Kafka for our communication bus;
• Modern observability solutions (such as Datadog or Grafana);
• Git, ArgoCD, Terraform, Kubernetes.

About you: 

As a Junior Software Engineer, you will have: 

• Strong sense of ownership and accountability (you are able to independently drive your work to the successful end and ask for help if needed).
• Master degree in Computer Science, Engineering, or a related field, OR equivalent practical experience (approx 1-2 years of professional experience in relevant role).
• Intermediate knowledge of one or more programming languages.
• Experience with Python on a real-world project.
• Experience with code versioning (Git).
• Experience with key software engineering concepts like unit tests, databases.
• At least basic experience with working in cloud environment (preferably GCP, AWS or Azure).
• Ability to work effectively in a team and communicate clearly.

Nice to haves: 

• Experience working in a multi-disciplinary team of Data Scientists, ML Engineers, SREs and product managers.
• Experience working with distributed computing.
• Experience with a real-world production grade system(s) and its deployment.
• Experience working with knowledge graphs or graph databases.
• Experience with data analysis, relevant machine learning techniques and graph algorithms.
• Experience with agile methodologies in software development.
• Experience in development using Java or Kotlin.

What’s in it for you? 

• Equity as we want you to have a part of what we are building.
• Unlimited Time Off Policy - A work-life balance and focus on our well-being are critical to keeping us performing at our best. 
• We embrace a hybrid approach that requires employees to be in the office for two days a week. We strongly believe that this approach fosters collaboration and enables the building of meaningful relationships.
• You will also get a new starter budget to kit out your home office. 
• Opportunity to work on innovative projects with smart-minded people keen to share their knowledge and continuously improve. 
• Annual learning budget (prorated based on start date) to drive your performance and career development.