Data Engineering Technical Lead
Hillenbrand, Inc.3 months ago
Remote, IndiaStaff+
Responsibilities
- Serve as the primary technical lead and escalation point for enterprise data engineering initiatives.
- Translate business requirements, reporting needs, and architectural standards into practical engineering guidance.
- Guide implementation decisions for Databricks pipelines, transformations, data models, reusable patterns, medallion architecture, and gold-layer datasets.
- Review engineering implementations for consistency, scalability, maintainability, and standards alignment.
- Troubleshoot data quality issues, failed pipelines, performance concerns, and production defects through root cause analysis.
- Lead L1/L2 operational support for enterprise data platform issues.
- Perform data lineage, dependency, and downstream impact analysis for pipeline and data model changes.
- Coordinate defect triage, release support, deployment validation, and production stabilization.
- Support engineering standards, governance controls, CI/CD processes, release management, and operational best practices.
- Mentor and guide data engineers and document technical patterns, implementation standards, and support procedures.
Requirements
- 12+ years of experience in data engineering, analytics engineering, BI engineering, or related technical roles.
- Graduate in Engineering, Technology, or a related field, as stated in the posting.
- Hands-on experience with Databricks, Spark, Delta Lake, SQL, and Python.
- Strong understanding of enterprise data warehousing, dimensional modeling, and modern data platform concepts.
- Experience supporting enterprise-scale data platforms and engineering teams.
- Experience reviewing engineering implementations and guiding technical delivery.
- Experience with data lineage, dependency analysis, and downstream impact assessment.
- Strong troubleshooting, root cause analysis, technical problem-solving, communication, and cross-functional collaboration skills.
- Knowledge of DevOps, CI/CD, release management, and deployment processes.
- Preferred experience includes Power BI semantic models, Azure Data Factory, Azure Data Lake, metadata management, governance or catalog platforms, complex enterprise environments, and AI-assisted development or automation tools.
Tech Stack
Categories
Data Engineering