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:
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Design, build, and maintain efficient, reusable, and reliable architecture and code for data pipelines and data applications on AWS.
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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.
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Develop and manage ETL/ELT processes to collect, process, and store data from multiple sources, ensuring data quality, integrity, and security.
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Architect and implement end-to-end data solutions (ingestion, storage, integration, processing, access) on AWS, with a focus on data lakes and data warehouses.
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Participate in the architecture and system design discussions for high-scale data engineering projects.
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Independently perform hands-on development, unit testing, and participate in code reviews to ensure adherence to best practices.
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Implement serverless applications using AWS Lambda, API Gateway, Step Functions, and other AWS technologies.
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Migrate data from traditional relational databases, file systems, and APIs to AWS-based data lakes (S3), RDS, Aurora, and Redshift.
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Implement high-velocity streaming solutions using Amazon Kinesis, SQS, and Kafka (preferred).
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Architect and implement CI/CD strategies for enterprise data platforms.
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Collaborate with product, operations, QA, and cross-functional teams throughout the software development cycle.
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Stay abreast of new technology developments, implement POCs for new tools/technologies, and onboard them for real-world use cases.
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Identify and resolve performance issues and continuously optimize for cost, reliability, and scalability.
Required Qualifications:
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Bachelor’s degree in Computer Science, Software Engineering, MIS, or equivalent combination of education and experience.
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5+ years of experience implementing and supporting data lakes, data warehouses, and data applications on AWS for large enterprises.
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Strong programming experience with Python, Shell scripting, and SQL.
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Solid experience with AWS services: CloudFormation, S3, Athena, Glue, EMR/Spark, RDS, Redshift, DynamoDB, Lambda, Step Functions, IAM, KMS, Secrets Manager.
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Experience in serverless application development and data pipeline orchestration.
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Experience in system analysis, design, development, and implementation of data ingestion pipelines in AWS.
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Knowledge of ETL/ELT, data modeling, and big data technologies.
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Familiarity with data warehousing concepts and cloud-based architecture.
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Strong problem-solving skills and attention to detail.
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Excellent communication and teamwork abilities.