Role & responsibilities
a Manager (Band C1) in Data Engineering serves as a critical bridge between client business goals and high-performing technical delivery. In this role, you will lead the architecture, construction, and deployment of scalable, cloud-native data pipelines and modern data warehousing solutions.
You will combine hands-on expertise in Python, SQL, Snowflake, AWS, and Jenkins with technical project governance, mentoring engineers, ensuring delivery quality, and interfacing directly with key global stakeholders.
Key Responsibilities
1. Technical Delivery & Architecture (60%)
- Architect, build, and optimize enterprise-grade ELT/ETL pipelines to ingest structured and semi-structured data using Python and advanced SQL.
- Design, implement, and maintain scalable cloud data warehouses using Snowflake (leveraging features like virtual warehouses, time travel, zero-copy cloning, dynamic data masking, and performance clustering).
- Deploy cloud solutions utilizing AWS core services (S3, Glue, Lambda, EC2, CloudWatch, MWAA, IAM).
- Establish robust automated build, test, and release processes using Jenkins CI/CD pipelines to support enterprise data pipelines.
- Ensure data pipeline resilience, optimization, automated data checks, and end-to-end monitoring.
2. Team Leadership & Project Management (25%)
- Act as the C1 Tech Lead / Delivery Manager, guiding a pod of data engineers, developers, and analysts across execution lifecycles.
- Perform code reviews, enforce engineering best practices, and drive continuous improvement across data integration workflows.
- Estimate delivery effort, manage technical sprints, and ensure adherence to SLAs and client deliverables.
- Mentor and upskill junior team members in cloud analytics and modern data stack tools.
3. Stakeholder Management & Strategy (15%)
- Collaborate with business analysts, data science teams, and client stakeholders to translate business requirements into technical architectures.
- Participate in solution design discussions for new analytics opportunities and client onboarding projects.
Required Technical Skills & Qualifications
- Overall Experience: 7 to 11 years in Data Engineering, Data Warehousing, and Cloud Analytics, with at least 2+ years in a lead or managerial role.
- Snowflake: Expert-level experience in Snowflake design, query optimization, resource management, and secure data sharing.
- SQL: Advanced mastery of complex SQL development, performance tuning, indexing strategies, analytical functions, and schema modeling (Star/Snowflake schemas).
- Python: Strong proficiency in Python for building automated data pipelines, custom scripts, and data manipulation.
- AWS Services: Hands-on experience with AWS data ecosystem tools (AWS S3, Glue, Lambda, CloudWatch, EMR, or Redshift).
- CI/CD & DevOps: Proven track record in orchestrating deployment automation using Jenkins.
- Education: B.E. / B.Tech / M.E. / M.Tech / MCA in Computer Science, Information Technology, Data Engineering, or equivalent.
Good-to-Have Skills
- Experience with orchestration frameworks like Apache Airflow.
- Exposure to modern analytics engineering workflows (e.g., dbt).
- Certifications: Snowflake SnowPro Core/Advanced or AWS Certified Data Engineer / Solutions Architect.
- Prior exposure to domain-driven analytics (Insurance, BFSI, Healthcare, or Supply Chain).
Why Join EXL?
- Impact & Scale: Work with fortune 500 enterprises on mission-critical data platforms.
- Continuous Growth: Structured learning tracks, cloud certification sponsorships, and clear leadership career paths.
- Collaborative Culture: Work alongside world-class data scientists, consultants, and platform architects in a collaborative e