Data Engineering Technical Lead
Remote · India
Job Summary
Data Engineering Technical Lead provides senior technical execution leadership for enterprise data engineering initiatives, bridging business requirements with architecture and engineering execution. Responsibilities include serving as primary technical lead for Databricks pipelines, transformations, and data models; guiding implementation decisions; ensuring scalability, reliability, and governance across data platforms; mentoring data engineers; coordinating defect triage and production stabilization; and promoting CI/CD, governance controls, and operational best practices. The role combines hands-on technical work with leadership, problem-solving, and cross-team collaboration across engineers, analysts, BI teams, DevOps, and architects. Technical emphasis areas include Databricks, Spark/PySpark, Delta Lake, SQL, Python, Power BI, Azure Data Factory/Data Lake, Unity Catalog/Data Lineage, and related Azure DevOps CI/CD tooling.
Required Qualifications
- 12+ years of experience in data engineering, analytics engineering, BI engineering, or related technical roles
- Graduate in Engineering / Technology or related field
- Strong understanding of enterprise data warehousing, dimensional modeling, and modern data platform concepts
- Hands-on experience with Databricks, Spark, Delta Lake, SQL, and Python
- Experience supporting enterprise-scale data platforms and engineering teams
- Strong troubleshooting, root cause analysis, and technical problem-solving skills
- Experience reviewing engineering implementations and guiding technical delivery
- Experience with data lineage, dependency analysis, and downstream impact assessment
- Ability to translate business and reporting requirements into practical engineering guidance
- Strong communication and collaboration skills across business and technical teams
- Knowledge of DevOps, CI/CD, release management, and deployment processes
- Preferred: Experience with Power BI semantic models, Azure Data Factory, Azure Data Lake
- Experience with metadata management, governance, or catalog platforms
- Experience operating within large, complex, multi-business-unit enterprise environments
- Exposure to AI-assisted development, automation, or engineering productivity tools
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