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Remote and generalist Data Engineer title increases applicant density despite seniority and some niche tooling.
Core data engineering skills are transferable across industries, though advertising/media domain experience is optional.
Explicit 7–8+ years, specific DBs and tooling (dbt, ClickHouse, SQL Server/Postgres) and AWS preference create stringent filters.
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Design, build, and operate robust ETL/ELT data pipelines processing millions of daily data points using open-source tools like dbt, ClickHouse, Airflow, or Dagster.
Migrate and modernize data platform from Azure Databricks to an AWS-based flexible stack, ensuring minimal disruption to high-volume data delivery.
Prototype and evaluate new tools and technologies; write and optimize complex SQL across SQL Server and Postgres; implement CI/CD, testing, and monitoring to maintain data quality and reliability.
7–8+ years of hands-on data engineering experience building and operating production data pipelines.
Strong proficiency in SQL with hands-on experience in SQL Server and Postgres, including schema design and query optimization.
Proficient in Python or similar programming language for building and automating data pipelines.
Hands-on experience with cloud platforms, preferably AWS; Bachelor's or Master's degree in Computer Science, Engineering, or related field or equivalent practical experience.
Experienced in modern, open-source data stacks and able to evaluate and adopt the right tools fitting platform goals.
Detail-oriented engineer comfortable diving deep into pipeline mechanics, SQL tuning, and maintaining data reliability at scale.
Adaptable to cross-continental collaborations and familiar with domains like Advertising, Media, or Market Research is a plus but not mandatory.