Senior Data Engineer

Sigma Software; Warszawa; On-site; PLN 240,000 - 360,000; Full time

Job summary

Sigma Software is seeking a Senior Data Engineer in Warsaw to lead greenfield data platform initiatives. You will build cloud-native data platforms, migrate on-prem systems to the cloud, and shape AI-ready infrastructure. Collaborate with ML, Data Science, and Product teams, driving R&D on agentic AI architectures and event-driven pipelines.

You will turn architectural concepts into production-grade solutions, ensuring data quality and platform reliability across pipelines and platforms.

Qualifications

• 5+ years of professional experience in Data Engineering.
• Strong Python and SQL development skills for pipeline development and optimisation.
• Proficiency in Apache Spark / PySpark, including query optimisation and performance tuning.
• Hands-on experience with Databricks (preferred) or Snowflake.
• Experience with at least one major cloud provider: Azure (preferred), AWS, or GCP.
• Experience with stream processing technologies (Kafka, Spark Structured Streaming).
• Solid understanding of ETL/ELT patterns, data modelling (dimensional, Data Vault), and data warehousing.
• Experience with orchestration tools (Apache Airflow, Azure Data Factory, or equivalent).
• Knowledge of Infrastructure as Code (Terraform or equivalent).
• Understanding of production-grade system requirements: reliability, scalability, observability, and performance.
• Upper-Intermediate English level

Responsibilities

• Design and build scalable, cloud-native data platforms from greenfield to production.
• Implement near-real-time ingestion pipelines using event-driven patterns.
• Define and enforce platform standards, including Data Lake / Lakehouse principles, medallion architecture, and data contracts.
• Refactor and optimise existing Spark and PySpark scripts for performance and maintainability.
• Introduce best practices for code quality, testing, and CI/CD across data pipelines.
• Drive adoption of AI tooling and agentic workflows within the data engineering team.
• Ensure data quality, observability, and reliability across all pipelines and platforms.
• Develop self-service tooling and microservices to simplify platform usage for other teams.

Skills

5+ years

Python

SQL

Spark / PySpark

Databricks

Cloud (Azure / AWS / GCP)

Kafka

Airflow

Terraform

Data modelling

English

Tools

Databricks

Snowflake

Airflow

Terraform

Azure Data Factory

Job description

Are you passionate about building cutting-edge, AI-ready data platforms from the ground up? We are looking for a Senior Data Engineer to join our Data Engineering Team and lead high-impact, greenfield initiatives.

You will work on building modern cloud-native data platforms, migrating on-premises legacy systems to the cloud, and laying the architectural foundation for AI-ready data infrastructure.

In this role, you will collaborate closely with Machine Learning, Data Science, and Product teams, serving as a key technical contributor and thought leader. You will also drive R&D efforts around agentic AI architectures, event-driven systems, and LLM-ready data pipelines – turning architectural concepts into production-grade solutions.

Are you passionate about building cutting-edge, AI-ready data platforms from the ground up? We are looking for a Senior Data Engineer to join our Data Engineering Team and lead high-impact, greenfield initiatives.

You will work on building modern cloud-native data platforms, migrating on-premises legacy systems to the cloud, and laying the architectural foundation for AI-ready data infrastructure.

In this role, you will collaborate closely with Machine Learning, Data Science, and Product teams, serving as a key technical contributor and thought leader. You will also drive R&D efforts around agentic AI architectures, event-driven systems, and LLM-ready data pipelines – turning architectural concepts into production-grade solutions.

Job Description

• Design and build scalable, cloud-native data platforms from greenfield to production
• Implement near-real-time ingestion pipelines using event-driven patterns
• Define and enforce platform standards, including Data Lake / Lakehouse principles, medallion architecture, and data contracts
• Refactor and optimise existing Spark and PySpark scripts for performance and maintainability
• Introduce best practices for code quality, testing, and CI/CD across data pipelines
• Drive adoption of AI tooling and agentic workflows within the data engineering team
• Ensure data quality, observability, and reliability across all pipelines and platforms
• Develop self-service tooling and microservices to simplify platform usage for other teams

Qualifications

• 5+ years of professional experience in Data Engineering
• Strong Python and SQL development skills for pipeline development and optimisation
• Proficiency in Apache Spark / PySpark, including query optimisation and performance tuning
• Hands-on experience with Databricks (preferred) or Snowflake
• Experience with at least one major cloud provider: Azure (preferred), AWS, or GCP
• Experience with stream processing technologies (Kafka, Spark Structured Streaming)
• Solid understanding of ETL/ELT patterns, data modelling (dimensional, Data Vault), and data warehousing
• Experience with orchestration tools (Apache Airflow, Azure Data Factory, or equivalent)
• Knowledge of Infrastructure as Code (Terraform or equivalent)
• Understanding of production-grade system requirements: reliability, scalability, observability, and performance
• Upper-Intermediate English level

WILL BE A PLUS

• Familiarity with RAG pipeline design and LLM integration patterns
• Knowledge of data governance frameworks and tools (Unity Catalog, Apache Atlas, or similar)
• Experience with dbt for data transformation and modelling
• Familiarity with MLflow, Feature Stores, or ML platform integration

Additional Information

PERSONAL PROFILE

• Self-driven and proactive in identifying improvements
• Comfortable working in a fast-paced, innovative environment
• Strong problem-solving mindset with attention to detail
• Open to experimenting with emerging technologies and approaches