Tech Lead - AWS Data Engineer

Location: Mumbai / Bangalore / Hyderabad / Gurgaon

Role Summary

We are looking for an experienced Tech Lead – AWS Data Engineering to lead the architecture, design, development, and delivery of enterprise-scale data platforms and modernization initiatives.

The role requires a strong combination of hands-on technical expertise, architectural decision-making, and exceptional team leadership capabilities. The individual will be responsible for leading and mentoring data engineering teams, defining scalable AWS-based data architectures, establishing engineering best practices, and ensuring high-quality delivery across complex enterprise engagements.

Responsibilities

1. Technical Leadership & Architecture

· Lead the architecture and implementation of enterprise-scale data platforms across AWS and Databricks, leveraging Medallion, Lakehouse, and Data Warehouse patterns.

· Define target-state architecture, data modeling standards, and reusable engineering frameworks, ensuring scalability, performance, security, and cost efficiency.

· Drive architectural decisions and design data platforms supporting advanced analytics, AI/ML, and Generative AI workloads.

2. AWS Data Engineering & Platform Development

· Design scalable data ingestion and processing frameworks supporting batch, streaming, real-time, and CDC pipelines using AWS services and Databricks.

· Establish engineering best practices for data integration, quality, performance optimization, observability, and production support.

· Drive platform optimization to improve processing efficiency, reliability, scalability, and cloud cost efficiency.

3. Team Leadership & People Management

· Lead, mentor, and manage data engineering teams, providing technical guidance, career development, and continuous learning opportunities.

· Own team execution, including sprint planning, task allocation, effort estimation, progress tracking, and delivery commitments.

· Drive engineering excellence through architecture reviews, code reviews, technical mentoring, and resolution of complex engineering challenges.

4. Technical Delivery & Engineering Excellence

· Own end-to-end technical delivery, from requirements gathering and solution design to development, deployment, and production stabilization.

· Establish CI/CD pipelines, automated testing, reusable frameworks, and engineering standards across data platforms.

· Proactively manage technical risks, resolve delivery blockers, and ensure adherence to timelines and quality standards.

5. Data Governance, Security & Compliance

· Define and implement enterprise data governance frameworks covering cataloging, lineage, stewardship, data quality, metadata management, and compliance.

· Establish secure data access patterns and governance standards leveraging tools such as Atlan, Unity Catalog, and AWS Lake Formation.

6. Client & Stakeholder Management

· Engage with clients as a trusted technical advisor, driving data strategy, architecture roadmaps, and solution design discussions.

· Translate business requirements into technical solutions and communicate architecture recommendations to senior stakeholders.

· Collaborate with cross-functional teams, manage stakeholder expectations, and identify opportunities for platform optimization and expansion of data and AI capabilities.

Qualifications

· 14+ years of experience in Data Engineering, Data Architecture, and Cloud Data Platforms, with proven leadership in large-scale enterprise data modernization initiatives.

· Strong hands-on expertise in AWS data services, including S3, Glue, EMR, Lambda, Redshift, Lake Formation, Athena, and DMS, along with advanced Databricks experience across Delta Lake, Unity Catalog, Databricks SQL, and Delta Live Tables.

· Deep expertise in data architecture and engineering, including Data Warehousing, Data Modeling (Dimensional, Data Vault), MDM, Data Quality, Metadata Management, and Data Governance.

· Strong understanding of batch, streaming, real-time, and CDC architectures, with experience in technologies such as Kafka, Kinesis, Apache Airflow, and MWAA.

· Proven experience leading and managing data engineering teams, with strong capabilities in technical mentoring, people development, architecture decision-making, and end-to-end delivery ownership.

· Experience implementing CI/CD pipelines, infrastructure automation, and engineering best practices for scalable, secure, and high-performance data platforms.

· Hands-on experience enabling AI/ML and Generative AI workloads, including AI-ready data foundations, feature layers, vector storage, and secure data access patterns.

· Strong client-facing and stakeholder management skills, with the ability to lead technical discussions, communicate architecture recommendations, and drive enterprise data strategy.

· AWS, Databricks, and/or Snowflake certifications preferred.