Job Description :

Manage data engineering projects, ensuring alignment with business objectives. Provide strategic guidance on data engineering best practices. Oversee a team of data engineers. Ensure continuous improvement of data processes.

Key Responsibilities :

- Design, build, and maintain efficient, reusable, and reliable architecture and code for data pipelines and data applications on AWS.

- Build robust data ingestion pipelines (from on-prem to AWS and within AWS) using AWS services such as Glue, Redshift, S3, Lambda, EMR/Spark, Kinesis, and SQS.

- Develop and manage ETL/ELT processes to collect, process, and store data from multiple sources, ensuring data quality, integrity, and security.

- Architect and implement end-to-end data solutions (ingestion, storage, integration, processing, access) on AWS, with a focus on data lakes and data warehouses.

- Participate in the architecture and system design discussions for high-scale data engineering projects.

- Independently perform hands-on development, unit testing, and participate in code reviews to ensure adherence to best practices.

- Implement serverless applications using AWS Lambda, API Gateway, Step Functions, and other AWS technologies.

- Migrate data from traditional relational databases, file systems, and APIs to AWS-based data lakes (S3), RDS, Aurora, and Redshift.

- Implement high-velocity streaming solutions using Amazon Kinesis, SQS, and Kafka (preferred).

- Architect and implement CI/CD strategies for enterprise data platforms.

- Collaborate with product, operations, QA, and cross-functional teams throughout the software development cycle.

- Stay abreast of new technology developments, implement POCs for new tools/technologies, and onboard them for real-world use cases.

- Identify and resolve performance issues and continuously optimize for cost, reliability, and scalability.

Required Qualifications :

- Bachelors degree in Computer Science, Software Engineering, MIS, or equivalent combination of education and experience.

- 5+ years of experience implementing and supporting data lakes, data warehouses, and data applications on AWS for large enterprises.

- Strong programming experience with Python, Shell scripting, and SQL.

- Solid experience with AWS services : CloudFormation, S3, Athena, Glue, EMR/Spark, RDS, Redshift, DynamoDB, Lambda, Step Functions, IAM, KMS, Secrets Manager.

- Experience in serverless application development and data pipeline orchestration.

- Experience in system analysis, design, development, and implementation of data ingestion pipelines in AWS.

- Knowledge of ETL/ELT, data modeling, and big data technologies.

- Familiarity with data warehousing concepts and cloud-based architecture.

- Strong problem-solving skills and attention to detail.

- Excellent communication and teamwork abilities.