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Remote mid-level data engineer role, popular title, and metro talent pool increase applicant competition.
Core data engineering skills are transferable, though AdTech/media measurement familiarity is preferred.
Explicit 4–8 years plus mandatory AWS, Spark, Airflow, and ETL expertise increases filtering strictness.
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Design, build, and maintain scalable data enrichment pipelines and AWS data architectures using Spark, Glue, Athena, and Airflow for a global media measurement client.
Perform deep data quality assessments, define/enforce data contracts and monitor data accuracy within high-scale, privacy-aware advertising data systems.
Troubleshoot multi-layered data delivery and device tracking issues, optimize pipeline performance and collaborate with global cross-functional teams for backend support.
4–8 years of data engineering experience with strong AWS-based data stack expertise (S3, Glue, Athena, Airflow).
Bachelor's degree in Computer Science or related field; advanced AI/ML exposure is a plus.
Proficiency in Apache Spark/PySpark, Python or Java, and SQL query writing/optimization.
Experience with media measurement or digital advertising data pipelines and integrating data via APIs, event streaming or flat files.
Experienced data engineer focused on analytical evaluation of large-scale, complex data sets with proven pipeline development in AWS environments.
Comfortable working on backend data infrastructure without front-end coding responsibilities, with curiosity about tracking and measurement logic in adtech ecosystems.
Able to independently troubleshoot technical and data quality issues across distributed, hybrid server-to-server architectures while collaborating with global product and data science teams.