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Metro-based Data Engineer role with common title and broad skill requirements creates moderate competition.
Core data engineering skills (ETL, Snowflake, Airflow) are widely transferable across industries.
Explicit 7–10 years plus mandatory Snowflake, ADF, Airflow, and dbt skills create strict shortlisting filters.
Lead the design, development, and maintenance of scalable, high-performance data pipelines and ETL/ELT frameworks.
Architect and integrate complex data workflows across cloud and on-premise sources ensuring data integrity and reliability with SLAs.
Collaborate with cross-functional teams to define data models, enforce best practices, monitor pipeline performance, and support data-driven business insights.
7 to 10 years of hands-on data engineering experience building scalable and secure data platforms.
Proficient in Python scripting with over 5 years experience for data processing and ETL development.
At least 4 years of Snowflake experience including performance tuning and advanced SQL.
Experience with data integration tools like Azure Data Factory, Fivetran, or Matillion, and cloud platforms such as Azure or AWS.
Experienced in operating within complex enterprise environments combining cloud and on-premise data sources.
Skilled in optimizing and monitoring data pipelines ensuring SLAs and high data quality for business insights.
Comfortable leading technical discussions bridging data engineering with analytics and business teams.