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Generalist Data Engineer title, metro location, and mid-level experience create high applicant competition.
Core Azure data engineering skills transfer across industries, though manufacturing domain preference raises sensitivity to medium.
Specific Azure Data Platform, ETL/ELT, and BI tech requirements mean candidates will be strictly filtered.
Design, build, maintain, and optimize scalable data pipelines, data warehouses, and semantic layers for Business Intelligence, analytics, AI, and operational reporting across Lifecycle Services (LCS).
Collaborate with BI developers, analysts, IT, and digital teams to ensure data accuracy, accessibility, security, scalability, and support for data-driven decision making.
Develop and implement data governance, quality controls, and compliance processes to enable trusted data foundations and support advanced analytics, AI, and automation initiatives.
Engineering graduate (B.Tech/BE in CS/IT/ECC).
Strong experience in data engineering, data architecture, and enterprise analytics environments (4+ years preferred but not explicitly required).
Advanced SQL and relational database design skills; experience with ETL/ELT pipeline development and maintenance.
Experience with Azure Data Platform technologies including Azure Data Factory, Azure Synapse, Azure Databricks, Microsoft Fabric, Dataverse, Azure SQL, and support for Power BI and semantic data layers.
Experienced in enterprise-scale data engineering roles with familiarity in ERP, CRM, service management, or financial systems integration.
Comfortable working in analytics-driven environments requiring collaboration with BI, IT, and compliance teams to enable data governance and advanced analytics.
Operates effectively in hybrid work setup with ability to juggle data architecture, integration, and automation initiatives within manufacturing or industrial automation contexts.