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Data Engineer
Warsaw, Masovian Voivodeship, Poland
Actively hiring, first seen by us today- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: Remote
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingDesign, build and maintain scalable data platforms and pipelines
Build distributed data processing workflows with Apache Spark, pySpark and Databricks
Integrate data from multiple heterogeneous sources with quality and traceability checks
Implement data quality controls, schema validation, lineage tracking and monitoring
Optimize pipeline performance, reliability and cost-efficiency
From the employer’s posting
Design, develop, and maintain scalable and reliable data platforms and pipelines for analytics and operational use cases
Build distributed data processing workflows using Apache Spark, pySpark, and Databricks
Integrate data from multiple heterogeneous sources while ensuring data quality, consistency, and traceability
Implement data quality controls, schema validation, lineage tracking, and monitoring processes
Optimize performance, scalability, reliability, and cost-efficiency of data pipelines and infrastructure
What you’ll bring
All qualificationsCore experience
- 3+ years of commercial experience in Data Engineering
- Experience packaging workloads using Docker and running batch workloads in AWS
- 2+ years of hands-on production experience with Databricks
- Familiarity with ECR and containerized data processing solutions
- Strong expertise with Delta Lake, including medallion architecture and table optimization
- Experience with experiment optimization libraries such as scikit-optimize, BoTorch, Ax, or scipy
Qualification wording
3+ years of commercial experience in Data Engineering
Experience packaging workloads using Docker and running batch workloads in AWS
2+ years of hands-on production experience with Databricks
Familiarity with ECR and containerized data processing solutions
Strong expertise with Delta Lake, including medallion architecture and table optimization
Experience with experiment optimization libraries such as scikit-optimize, BoTorch, Ax, or scipy
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 3, 2026today
- Recorded sightings
- 2
- Last seen by us
- Nov 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Sigma Software is hiring
35 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Data Engineer hiring is picking up
- Takes jobs down quickly
- 3% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- AWS
- Azure
- Databricks
- Delta Lake
- Docker
- MLflow
- S3
- Spark
- Terraform
- PySpark
- scikit-learn
- SciPy
- Great Expectations
Where the posting names them
• Strong expertise with Delta Lake, including medallion architecture and table optimization • Advanced proficiency in Python, pySpark, and SQL • Deep practical knowledge of Databricks components including Unity Catalog, Lakeflow Jobs / Workflows, and job optimization
• Build distributed data processing workflows using Apache Spark, pySpark, and Databricks • Develop and evolve cloud-native data solutions leveraging Azure and AWS services • Model and optimize data structures for analytics, reporting, and downstream consumption
• Deep practical knowledge of Databricks components including Unity Catalog, Lakeflow Jobs / Workflows, and job optimization • Hands-on experience with AWS services including S3, IAM, KMS, and at least one of AWS Batch, ECR, EventBridge, Lambda, or API Gateway • Experience designing event-driven and asynchronous data pipelines
WILL BE A PLUS: • Experience packaging workloads using Docker and running batch workloads in AWS • Familiarity with ECR and containerized data processing solutions
Join a project shaping the future of sustainable energy through cutting-edge data engineering and scientific innovation. We are looking for a Data Engineer to help build scalable cloud-native data platforms for a deep-tech Customer working on one of the most ambitious fusion energy initiatives in the world. You will work with modern distributed data technologies, including Databricks, Apache Spark, AWS, and Azure, while solving complex engineering challenges in a research-intensive environment. We at Sigma Software value engineering excellence, long-term partnerships, and continuous learning. This is an opportunity to contribute to breakthrough scientific initiatives while working with modern technologies and highly skilled global teams.
• Design, develop, and maintain scalable and reliable data platforms and pipelines for analytics and operational use cases • Build distributed data processing workflows using Apache Spark, pySpark, and Databricks • Develop and evolve cloud-native data solutions leveraging Azure and AWS services
• 3+ years of commercial experience in Data Engineering • 2+ years of hands-on production experience with Databricks • Strong expertise with Delta Lake, including medallion architecture and table optimization
• Advanced proficiency in Python, pySpark, and SQL • Deep practical knowledge of Databricks components including Unity Catalog, Lakeflow Jobs / Workflows, and job optimization • Hands-on experience with AWS services including S3, IAM, KMS, and at least one of AWS Batch, ECR, EventBridge, Lambda, or API Gateway
• Strong knowledge of Infrastructure as Code and CI/CD practices • Practical experience with Terraform and Databricks Asset Bundles • Experience working with Git workflows, pull request reviews, and automated testing
• Strong understanding of data quality, lineage, schema contracts, and provenance concepts • Experience with data quality frameworks such as Great Expectations, Soda, pyDeequ, or Databricks DQX • Experience monitoring and optimizing data platform reliability and cloud costs
• 2+ years of hands-on production experience with Databricks • Strong expertise with Delta Lake, including medallion architecture and table optimization • Advanced proficiency in Python, pySpark, and SQL
• Understanding of secure authentication approaches such as OIDC and service principals • Practical experience with MLflow, experiment tracking, model registry, and reproducibility practices • Strong understanding of data quality, lineage, schema contracts, and provenance concepts
• Familiarity with ECR and containerized data processing solutions • Experience with experiment optimization libraries such as scikit-optimize, BoTorch, Ax, or scipy • Understanding of ML feature engineering and leakage prevention techniques
Your next step
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Already applied? Track this application
Has Sigma Software posted this job before?
Yes. We’ve followed 2 other Data Engineer jobs there since September 2026. All 2 are still up.
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
Job Description
• Design, develop, and maintain scalable and reliable data platforms and pipelines for analytics and operational use cases
• Build distributed data processing workflows using Apache Spark, pySpark, and Databricks
• Develop and evolve cloud-native data solutions leveraging Azure and AWS services
• Model and optimize data structures for analytics, reporting, and downstream consumption
• Integrate data from multiple heterogeneous sources while ensuring data quality, consistency, and traceability
• Implement data quality controls, schema validation, lineage tracking, and monitoring processes
• Optimize performance, scalability, reliability, and cost-efficiency of data pipelines and infrastructure
• Collaborate closely with scientists, analysts, and engineering teams to translate business and research requirements into technical solutions
• Contribute to CI/CD processes, infrastructure automation, and engineering best practices
• Research and introduce new technologies and approaches within the data engineering ecosystem
Qualifications
• 3+ years of commercial experience in Data Engineering
• 2+ years of hands-on production experience with Databricks
• Strong expertise with Delta Lake, including medallion architecture and table optimization
• Advanced proficiency in Python, pySpark, and SQL
• Deep practical knowledge of Databricks components including Unity Catalog, Lakeflow Jobs / Workflows, and job optimization
• Hands-on experience with AWS services including S3, IAM, KMS, and at least one of AWS Batch, ECR, EventBridge, Lambda, or API Gateway
• Experience designing event-driven and asynchronous data pipelines
• Strong knowledge of Infrastructure as Code and CI/CD practices
• Practical experience with Terraform and Databricks Asset Bundles
• Experience working with Git workflows, pull request reviews, and automated testing
• Understanding of secure authentication approaches such as OIDC and service principals
• Practical experience with MLflow, experiment tracking, model registry, and reproducibility practices
• Strong understanding of data quality, lineage, schema contracts, and provenance concepts
• Experience with data quality frameworks such as Great Expectations, Soda, pyDeequ, or Databricks DQX
• Experience monitoring and optimizing data platform reliability and cloud costs
• Upper-Intermediate or higher English level
WILL BE A PLUS:
• Experience packaging workloads using Docker and running batch workloads in AWS
• Familiarity with ECR and containerized data processing solutions
• Experience with experiment optimization libraries such as scikit-optimize, BoTorch, Ax, or scipy
• Understanding of ML feature engineering and leakage prevention techniques
• Experience working in regulated or high-security environments
• Knowledge of data governance, compliance, and data residency requirements
• Experience disabling or restricting AI-assisted tooling/features in secure environments
Company Description
Join a project shaping the future of sustainable energy through cutting-edge data engineering and scientific innovation. We are looking for a Data Engineer to help build scalable cloud-native data platforms for a deep-tech Customer working on one of the most ambitious fusion energy initiatives in the world.
You will work with modern distributed data technologies, including Databricks, Apache Spark, AWS, and Azure, while solving complex engineering challenges in a research-intensive environment.
We at Sigma Software value engineering excellence, long-term partnerships, and continuous learning. This is an opportunity to contribute to breakthrough scientific initiatives while working with modern technologies and highly skilled global teams.
PROJECT
The project focuses on building and evolving a modern cloud-based data platform supporting scientific research, analytics, and operational workflows in the fusion energy domain. The team develops scalable distributed data processing solutions while ensuring high standards of data quality, lineage, governance, reproducibility, and operational reliability.
You will work closely with scientists and engineers to support experimentation, analytics, and machine learning workflows in a highly innovative R&D environment.
Source details
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- Pay
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- Location & working pattern
Warsaw, Masovian Voivodeship, Poland
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.