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Generalist data engineering role, metro location, broad cloud/ETL skillset, mid experience range.
Core data engineering skills are highly transferable across industries, yielding low background sensitivity.
Explicit 4–10 years plus mandatory SQL, Python/PySpark, cloud, and orchestration tools increases filter strictness.
Design, develop, and maintain scalable ETL/ELT data pipelines for diverse data sources supporting analytics and AI/ML initiatives.
Build and optimize data models, warehouses, and automate data workflows on cloud platforms such as AWS, GCP, or Azure.
Collaborate with Data Architects, Scientists, and business teams; troubleshoot data quality and pipeline performance; mentor junior engineers (scope depends on seniority).
4–10 years of experience in Data Engineering or related roles.
Proficient in advanced SQL, Python and/or PySpark for data processing and automation.
Experience with cloud data platforms (AWS, GCP, or Azure) and data warehouse/lake technologies (Snowflake, BigQuery, Databricks, Microsoft Fabric).
Degree in Computer Science, Engineering, or related quantitative field, or equivalent practical experience.
Experienced in end-to-end ownership of cloud-native data engineering pipelines and workflow orchestration tools like Airflow, dbt, or Cloud Composer.
Able to work cross-functionally with technical and business stakeholders to translate requirements into scalable data solutions.
Has prior exposure to advanced data engineering domains such as Marketing/Media data, GenAI adjacent engineering, Databricks DataOps, Microsoft Fabric, or team leadership roles for mentoring and delivery ownership.