Manager – Data Engineering

Key Deliverables in this role

Data Pipelines & ELT/ETL Engineering

• Design, build, and maintain end-to-end ETL/ELT pipelines for structured and semi-structured data.
• Develop and enhance integrations using SnapLogic and AWS Glue with reusable components and standard patterns.
• Implement scalable transformations using SQL and Python/PySpark, ensuring correctness and maintainability.
• Support incremental and batch processing patterns with robust error handling and recovery.

Cloud Data Platform (S3 + Snowflake)

• Build cloud-native data solutions using AWS S3 for storage and Snowflake for analytics-ready datasets.
• Develop efficient loading patterns into Snowflake and optimize performance and cost.
• Create curated, consumption-ready datasets that serve dashboards, reporting, and downstream ML workloads.

Workflow Orchestration & Automation (Airflow)

• Orchestrate and monitor production workflows using Apache Airflow (scheduling, dependencies, retries, alerts).
• Implement operational automation to improve reliability, reduce manual effort, and increase repeatability.
• Troubleshoot failures quickly and drive resolution to meet defined delivery SLAs.

Data Quality, Governance & Operational Excellence

• Leverage AI/ML to innovation and learnings from market into creating seamless customer experience
• Champion agile methodologies, steering our Sales digital transformation projects with precision and adaptability, ensuring that we deliver value fast, and pivot even faster when needed.

Collaboration & Delivery

• Partner with analytics, reporting, and data scienceteams to understand consumption needs and deliver trusted data.
• Participate in design and code reviews; contribute to improving engineering standards and platform practices. Support release management practices including versioning, testing, and controlled rollouts.

Critical success factors for the role

• 56 years of hands-on experience in Data Engineering / ETL / ELT roles.
• Proven experience building and operating production-grade pipelines with monitoring and incident resolution.
• Experience working with cloud-scale data sets and performance-oriented data processing.

Desirable success factors for the role

Exposure to AI/ML fundamentals and enabling ML-ready data sets (feature consistency, stable metrics).

Familiarity with data governance / metadata practices (catalog, lineage, stewardship).

Experience with additional AWS services (e.g. ,Lambda, Redshift) or Azure/GCP.

Relevant certifications: AWS / Snowflake / SnapLogic.

Core Technical Skills

• SQL: Advanced SQL for transformations, reconciliation, and query optimization.
• Python / PySpark: Production-quality coding for scalable processing and transformations.
• ETL/ELT Tools: Hands-on experience with SnapLogic and/or similar integration tools.
• AWS Data Services: Strong working knowledge of AWS Glue andS3 (ingestion, processing, storage).
• Snowflake: Experience with Snowflake warehousing, loading strategies, and performance tuning.
• Orchestration: Production usage of Apache Airflow for scheduling, dependency control, and monitoring.
• Reliability & Ops: Ability to troubleshoot failures, improve observability, and stabilize pipelines.
• Version Control: Strong Git proficiency and standard code review / branching workflows.