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Popular Data Engineer role with unspecified experience and common stack increases applicant competition.
Data engineering skills (Airflow, SQL, Python, Snowflake) are reasonably transferable across industries.
Mandatory technical skills (Airflow, Python, SQL, Snowflake) but no years requirement makes screening moderately strict.
Design, build, and operate production-grade data pipelines using Apache Airflow, managing complex, multi-dependency DAGs end-to-end.
Own ingestion and transformation logic along with scheduling, monitoring, failure recovery, and performance tuning of pipelines.
Collaborate closely with data architects, analysts, and business stakeholders to improve pipeline engineering standards.
Strong hands-on experience with Apache Airflow for data pipeline orchestration.
Proficient in production-quality Python programming.
Strong SQL skills including analytical functions, complex joins, and query optimization.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in debugging real pipeline failures with understanding of idempotency, backfills, and retry strategies.
Comfortable handling end-to-end ownership of complex data pipeline delivery in a production environment.
Skilled in collaborating with cross-functional teams to raise engineering standards in data pipelines.