Back
Metro location, common data engineering manager title, and broad technical skillset increase candidate density.
Core data engineering skills are transferable but enterprise data governance and domain-specific ERP/R&D experience add specificity.
Explicit 10+ years requirement plus mandated cloud, Databricks/Snowflake/DBT, SQL/PySpark and governance skills.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead technology and data initiatives to enhance business value across multiple internal and external data domains such as ERP, HR, Customer, R&D, and Consumer.
Own design, implementation, and governance of data integration, acquisition, ingestion, and common data models to enable effective large dataset analysis.
Collaborate cross-functionally with business units and technology teams to drive unified development, adoption, and governance of data products aligned to organizational impact and roadmap evolution.
10+ years in IT specializing in Data & Technology Architecture, Data Engineering, and Data Operations.
Undergraduate degree in Technology, Computer Science, Statistics, Economics, applied data sciences or related field; advanced degree preferred.
Minimum 5 years hands-on experience with Cloud Architecture (Azure, GCP, AWS), cloud-based databases (Synapse, Databricks, Snowflake), and data integration tools (DBT, SQL/PySpark, Python).
Strong knowledge in Data Governance, Data Quality principles, Master Data Management, Data Harmonization, Data Cataloging, and Data Architecture.
Senior-level data engineering professional with advanced hands-on expertise and experience implementing AI-driven data processing and ML pipelines.
Experienced in bridging technical and functional teams, able to align data initiatives with business objectives and technical requirements.
Proven ability to lead data product development in complex environments using agile methodology, data mesh/fabric concepts, and deliver scalable, governed data capabilities.