Aws Data Engineer

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

Preferred candidate profile