Data Engineering Technical Lead in India at Jobgether

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Data Engineering Technical Lead in India.

This role acts as the senior technical backbone of a large-scale enterprise data engineering ecosystem, ensuring reliable, scalable, and well-governed data solutions across multiple business domains. You will bridge architecture, business requirements, and hands-on engineering execution, guiding teams through complex implementation challenges. The position combines deep technical expertise with operational leadership, focusing on Databricks-based data platforms, pipeline reliability, and production stability. You will play a key role in troubleshooting, optimizing, and standardizing data engineering practices across distributed teams. The environment is highly collaborative and fast-moving, requiring strong coordination across BI, architecture, DevOps, and engineering functions. This is a high-impact leadership role where your decisions directly shape data quality, platform performance, and enterprise analytics outcomes.

• Act as the primary technical lead and escalation point for enterprise data engineering initiatives, ensuring successful delivery of complex data solutions.
• Translate business and reporting requirements into actionable engineering guidance across Databricks, Spark, and modern data platforms.
• Guide the design and implementation of data pipelines, transformations, and scalable data models aligned with enterprise standards.
• Review engineering solutions for performance, scalability, maintainability, and architectural consistency across teams.
• Lead troubleshooting, root cause analysis, and resolution of data pipeline failures, performance issues, and data quality incidents.
• Perform lineage analysis and downstream impact assessment for data model changes and platform updates.
• Support L1/L2 operational stability, including production monitoring, defect triage, and release validation activities.
• Define and promote reusable engineering patterns, medallion architecture practices, and standardized gold-layer datasets.
• Drive adoption of CI/CD, DevOps practices, governance frameworks, and engineering best practices across teams.
• Mentor and guide data engineers, ensuring consistency in implementation approaches and technical execution quality.
• Document technical standards, operational procedures, and reusable engineering frameworks to improve platform maturity.

• 12+ years of experience in data engineering, analytics engineering, BI engineering, or related technical roles.
• Strong expertise in enterprise data warehousing, dimensional modeling, and modern data architecture principles.
• Hands-on experience with Databricks, Spark/PySpark, Delta Lake, SQL, and Python.
• Proven experience supporting and scaling enterprise-grade data platforms in complex environments.
• Strong skills in troubleshooting, root cause analysis, and performance optimization of data pipelines.
• Experience reviewing engineering implementations and guiding technical design decisions.
• Solid understanding of data lineage, dependency mapping, and downstream impact analysis.
• Ability to translate business needs into clear technical solutions and engineering direction.
• Strong communication and collaboration skills across technical and non-technical stakeholders.
• Experience with CI/CD pipelines, DevOps practices, and deployment/release management processes.
• Preferred experience with Azure Data Factory, Azure Data Lake, Power BI semantic models, and data governance tools.
• Exposure to large-scale, multi-team enterprise environments and modern data platform ecosystems.

• Competitive compensation aligned with senior technical leadership responsibilities.
• Flexible work arrangements supporting work-life balance.
• Opportunity to lead enterprise-scale data engineering transformation initiatives.
• Exposure to modern cloud data platforms including Databricks and Azure ecosystem technologies.
• Strong focus on professional development, mentorship, and technical leadership growth.
• Collaborative, cross-functional environment with global engineering teams.
• Participation in large-scale data modernization and analytics programs.
• Comprehensive benefits including healthcare and employee well-being support.