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Mid-level data role, metro location, and generalist engineering requirements increase applicant density.
Python, SQL, ETL, and orchestration skills are highly transferable across industries.
Explicit 3+ years requirement plus mandatory Python, SQL, and orchestration skills enforce strict filtering.
Own and enhance production data pipelines including ingestion from internal and external sources like APIs, exchanges, and web scraping.
Monitor and troubleshoot pipeline execution failures, perform root cause analysis, and ensure pipeline health via centralized monitoring and observability reporting.
Design and maintain robust data models, SQL workflows, database schemas, and support data governance and production incident investigations.
3+ years of professional Data Engineering experience.
Strong Python skills for ETL, APIs, data processing, automation, and production workflows.
Strong SQL skills with experience in database and data-model design and optimization.
Experience with workflow orchestration/scheduling tools (e.g., Jenkins, Airflow, Azure Data Factory) and implementing pipeline monitoring, logging, alerting, and data quality controls.
Experienced in managing end-to-end data pipeline ownership with focus on production reliability and observability.
Proficient in both development (Python, SQL) and operational aspects (orchestration, logging, alerting) of data workflows.
Comfortable working in collaborative, Git-based environments with code reviews and version control best practices.