Data Engineer (ETL, Snowflake, AWS Glue)

Roles & Responsibilities:

• Develop tools to improve data flows between internal/external systems and the data lake/warehouse.
• Work with stakeholders to understand needs for data structure, availability, scalability, and accessibility.
• Build robust and reproducible data ingest pipelines to collect, clean, harmonize, merge, and consolidate data sources.
• Understanding existing data applications and infrastructure architecture
• Build and support new data feeds for various Data Management layers and Data Lakes
• Evaluate business needs and requirements.
• Support migration of existing data transformation jobs in Oracle, and MS-SQL to Snowflake.
• Lead the migration of the existing data transformation jobs in Oracle, Hive, Impala etc. into Spark, Python on Glue etc.
• Able to document the processes and steps.
• Develop and maintain datasets.
• Improve data quality and efficiency.
• Lead Business requirements and deliver accordingly.
• Collaborate with Data Scientists, Architect and Team on several Data Analytics projects.
• Collaborate with DevOps Engineer to improve system deployment and monitoring process.
• Experience in critical production support and how the BAU functions is preferred.

Key Requirements:

• Lead a team of data engineers in managing day-to-day BAU operations, production support, incident management, and platform stability.
• Strong AWS knowledge in terms of designing new architecture and providing optimized solutions for existing ones. (S3, Glue, DMS, MWAA, AMS, IAM).
• In-depth knowledge with respect to Snowflake and its architecture.
• Prefer prior Experience in BAU environment.
• Good knowledge on Airflow and MWAA.
• Hands-on experience in SQL/Python/Pyspark.
• Expertise in optimizing techniques in cloud environments.
• Should have the vision on data strategy and able to deliver the same.

Must Have: AWS - Glue, Airflow, Lambda, S3 , Snowflake (Related Cloud DB) SQL ,Python/ Spark,CI/CD Good To have : ECS,GIT, Oracle