Senior Data Engineer (ETL, ML Experience)

Location: Remote (Europe preferred)
Contract Type: B2B
Experience: 7+ years as a Data Engineer
English Level: C1 (Advanced)
Compensation: Gross (to be specified)
Holidays: 10 public holidays per year (vacation and sick days unpaid)

About the Role

We are seeking a Senior Data Engineer with strong experience in ETL pipeline design, data analytics, and exposure to machine learning workflows. You will play a key role in designing, developing, and maintaining scalable data solutions to support analytics, reporting, and ML-driven decision-making.

You will work closely with data scientists, analysts, and software engineers to ensure data integrity, performance, and accessibility across the organization.

Key Responsibilities

• Design, build, and maintain ETL/ELT pipelines for large-scale data processing.

• Develop, optimize, and manage data models, data warehouses, and data lakes.

• Collaborate with cross-functional teams to define data architecture, governance, and best practices.

• Implement and maintain CI/CD workflows using AWS CodePipeline.

• Work with Python and .NET for automation, data integration, and application-level data handling.

• Support data-driven decision-making through analytics and reporting.

• Troubleshoot and optimize database performance and data processing pipelines.

• Implement data quality and validation frameworks to ensure reliable data flow.

Required Skills & Experience

• 7+ years of professional experience as a Data Engineer or similar role.

• Strong expertise in ETL development and orchestration.

• Python — Expert level (data processing, automation, APIs, ML pipeline integration).

• ETL Tools / Frameworks — Expert level (custom and/or AWS-native).

• Data Analytics & Reporting — Expert level (data modeling, KPI dashboards, insights generation).

• DBA experience — Experienced (database design, tuning, and maintenance).

• AWS CodePipeline — Experienced (CI/CD for data workflows).

• .NET — Experienced (integration, backend data logic).

• Experience with data warehousing solutions (e.g., Redshift, Snowflake, BigQuery) is a plus.

• Familiarity with machine learning data pipelines (feature engineering, data prep, model serving) is a plus.

Nice to Have

• Experience with Airflow, DBT, or other orchestration tools.

• Familiarity with Terraform or AWS CloudFormation.

• Exposure to ML Ops and productionizing ML models.

• Knowledge of data governance, security, and compliance standards.