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Senior Data Pipeline Engineer – Freelance
DecentriqSenior Data Pipeline Engineer building Python and Apache Spark pipelines for Decentriq’s confidential data-clean-room platform. Productionising AdTech ML models and improving orchestration, observability, and Spark performance.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and operating Spark-based data pipelines, with a strong focus on improving and productionizing machine learning models for AdTech applications. Proficient in leveraging orchestration frameworks and ensuring data quality throughout the ML lifecycle.
Highest-signal resume keywords
Expert-Level PythonPySpark/Scala Spark ExperienceData-Platform SkillsBuilding Resilient Data PipelinesOrchestration Frameworks
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Spark-Based Data PipelinesMachine Learning ModelsData Quality ChecksPython ProgrammingPySparkScalaRust ProficiencyAudience SegmentationLookalike ModellingBenchmarking and Tuning
Tools & Technologies
AirflowDagsterJob SchedulersLLM-Based Code AssistantsTest-Automation Tools
Certifications & Qualifications
Bachelor's DegreeMaster's DegreePhD in Computer ScienceData Engineering
Industry Keywords
AdTechML LifecycleProduction-Grade Data PipelinesOrchestrationObservability
Tech Stack
Tools & technologiesAirflowPySparkPythonRustScalaSpark
About the role
Key responsibilities & impact- Own, design, build, and operate Spark-based data pipelines from development through production and monitoring
- Improve and productionise ML models for AdTech use cases such as lookalike modelling and demographics modeling
- Leverage LLM-based code assistants, design generators, and test-automation tools to improve speed and quality
- Share AI-powered productivity workflows with the team
- Profile, benchmark, and tune Spark workloads
- Introduce best practices in orchestration and observability
- Keep the technology stack future-proof
Requirements
What you’ll need- Bachelor/Master/PhD in Computer Science, Data Engineering, or a related field
- 5+ years of professional experience
- Expert-level Python and PySpark/Scala Spark experience
- Proven track record building resilient, production-grade data pipelines with rigorous data-quality and validation checks
- Data-platform skills operating Spark clusters, job schedulers, or orchestration frameworks such as Airflow, Dagster, or custom schedulers
- Working knowledge of ML lifecycle and model serving is a plus
- Familiarity with audience segmentation or lookalike modelling is a plus
- Rust proficiency is a plus
Benefits
Comp & perks- Competitive rates
- Ownership instead of simply an executor
- An amazing and fun team that is distributed all over Europe
- Possible conversion into FTE after the 6-month freelance engagement
- No need for a formal motivational letter