Advanced Distributed Data Platform Engineer

Relativity; Białystok; Remote; PLN 146,000 - 218,000; Full time

Job summary

A leading data analytics firm in Poland is seeking an Advanced Data Platform Engineer to design and implement scalable, cloud-native data platforms. The ideal candidate will leverage technologies like Apache Spark and Delta Lake to build analytics capabilities. Responsibilities include developing data pipelines and collaborating with teams to deliver curated data. The expected salary ranges from 146,000 to 218,000 PLN, depending on experience and skills.

Benefits

Comprehensive health, dental, and vision plans

Parental leave for primary and secondary caregivers

Flexible work arrangements

Two, week-long company breaks per year

Unlimited time off

Long-term incentive program

Training investment program

Qualifications

• Strong programming skills in Python and SQL; experience with Apache Spark for distributed data processing.
• Expertise in Delta Lake and/or Apache Iceberg for lakehouse architecture.
• Familiarity with dbt, Databricks, and Snowflake for analytics workflows.

Responsibilities

• Design and implement complex data pipelines and distributed systems using Spark and Python.
• Develop and maintain lakehouse capabilities with Delta Lake and Iceberg, ensuring reliability and performance.
• Collaborate with internal teams to deliver curated datasets and self-service analytics capabilities.

Skills

Strong programming skills in Python

Experience with Apache Spark

Expertise in Delta Lake

Familiarity with dbt

Solid understanding of software engineering principles

Familiarity with Kubernetes

Tools

Databricks

Snowflake

Apache Iceberg

Python

Job description

Posting Type

Remote

Job Overview

We are building a specialized team focused on enabling advanced analytics and reporting capabilities across our internal data ecosystem. As an Advanced Data Platform Engineer, you will design and implement scalable, cloud-native data platforms that integrate modern lakehouse technologies, distributed compute frameworks, and cloud-native services to support diverse analytical use cases and enterprise-scale insights.

You will work on systems leveraging Apache Spark, Delta Lake, and Iceberg to process large-scale datasets efficiently, while enabling internal users to build reporting and analytics through curated data models, optimized query performance, and reliable data pipelines. This role emphasizes technical depth, performance optimization, and governance best practices to deliver secure and reliable solutions.

Relativity’s scale and breadth provide significant opportunities for rich data exploration and insights. Our data infrastructure ensures that vast datasets remain accessible, secure, and compliant, while enabling innovation across the organization. We are making substantial investments in data lake technology and distributed systems to support future growth and advanced analytics.

Your Role in Action

• Design and implement complex data pipelines and distributed systems using Spark and Python.
• Apply software engineering best practices: clean code, modular design, CI/CD, automated testing, and code reviews.
• Develop and maintain lakehouse capabilities with Delta Lake and Iceberg, ensuring reliability and performance.
• Enable analytics workflows by integrating dbt for SQL transformations running on Spark.
• Collaborate with internal teams to deliver curated datasets and self-service analytics capabilities.
• Optimized data warehousing solutions such as Databricks and Snowflake for performance and scalability.
• Implement observability and governance frameworks, including data lineage and compliance controls.
• Drive performance tuning, scalability strategies, and cost optimization across Spark jobs and cloud-native environments.

Core Requirements

• Strong programming skills in Python and SQL; experience with Apache Spark for distributed data processing.
• Expertise in Delta Lake and/or Apache Iceberg for lakehouse architecture.
• Familiarity with dbt, Databricks, and Snowflake for analytics workflows.
• Solid understanding of software engineering principles, CI/CD, and automated testing.
• Familiarity with Kubernetes, Docker, and infrastructure-as-code tools.
• Understanding of performance tuning, scalability strategies, and cost optimization for large-scale systems.

Nice to Have

• Exposure to event-driven architectures and advanced analytics platforms.
• Experience enabling self-service analytics for internal stakeholders.
• Experience in any of the following languages: Java, Scala, Rust.

Relativity is a diverse workplace with different skills and life experiences—and we love and celebrate those differences. We believe that employees are happiest when they're empowered to be their full, authentic selves, regardless how you identify.

Benefit Highlights

• Comprehensive health, dental, and vision plans
• Parental leave for primary and secondary caregivers
• Flexible work arrangements
• Two, week-long company breaks per year
• Unlimited time off
• Long-term incentive program
• Training investment program

Equal Employment Opportunity

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.

Relativity is committed to competitive, fair, and equitable compensation practices

This position is eligible for total compensation which includes a competitive base salary, an annual performance bonus, and long-term incentives.

The expected salary range for this role is between 146,000 and 218,000 PLN.

The final offered salary will be based on several factors, including but not limited to the candidate's depth of experience, skill set, qualifications, and internal pay equity. Hiring at the top end of the range would not be typical, to allow for future meaningful salary growth in this position.

Suggested Skills

• Engineering Principle
• Hardware Integration
• Innovation
• Problem Solving
• Process Improvements
• Quality Assurance (QA)
• Research and Development
• System Designs
• Technical Documents
• Troubleshooting