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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.
Accountabilities:
- 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.
Requirements:
- 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.
Benefits:
- 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.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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