AWS Data Engineer

Role Overview:

• Designing, building and maintaining efficient, reusable, and reliable architecture and code.
• Build reliable and robust Data ingestion pipelines (within AWS, onprem to AWS ,etc.)
• Ensure the best possible performance and quality of high scale data engineering project
• Participate in the architecture and system design discussions
• Independently perform hands on development and unit testing of the applications.
• Collaborate with the development team and build individual components into complex enterprise web systems.
• Work in a team environment with product, production operation, QE/QA and cross functional teams to deliver a project throughout the whole software development cycle.
• Responsible to identify and resolve any performance issues
• Keep up to date with new technology development and implementation
• Participate in code review to make sure standards and best practices are met.

Key Responsibilities & Skillsets:

• Common Skillsets:
  • Superior analytical and problem solving skills
  • Should be able to work on a problem independently and prepare client ready deliverable with minimal or no supervision
  • Good communication skill for client interaction

Application development Skillsets:

• Bachelor's degree in computer science, Software Engineering, MIS or equivalent combination of education and experience.
• Experience implementing, supporting data lakes, data warehouses and data applications on AWS for large enterprises.
• Programming experience with Python, Shell scripting and SQL.
• Solid experience of AWS services such as CloudFormation, S3, Athena, Glue, EMR/Spark, RDS, Redshift, DynamoDB, Lambda, Step Functions, IAM, KMS, SM etc.
• Solid experience implementing solutions on AWS based data lakes.
• Should have good experience with AWS Services - API Gateway, Lambda, Step Functions, SQS, DynamoDB, S3, Elasticsearch.
• Serverless application development using AWS Lambda.
• Experience in AWS data lake/data warehouse/business analytics.
• Experience in system analysis, design, development, and implementation of data ingestion pipeline in AWS.
• Knowledge of ETL/ELT.
• End-to-end data solutions (ingest, storage, integration, processing, access) on AWS.
• Architect and implement CI/CD strategy for EDP.
• Implement high velocity streaming solutions using Amazon Kinesis, SQS, and Kafka (preferred).
• Migrate data from traditional relational database systems, file systems, NAS shares to AWS relational databases such as Amazon RDS, Aurora, and Redshift.
• Migrate data from APIs to AWS data lake (S3) and relational databases such as Amazon RDS, Aurora, and Redshift.
• Implement POCs on any new technology or tools to be implemented on EDP and onboard for real use-case.
• AWS Solutions Architect or AWS Developer Certification preferred.
• Good understanding of Lakehouse/data cloud architecture.

Candidate Profile:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 7+ years of experience in data engineering, with at least 3 years in technical leadership or lead engineer role.
• Extensive hands-on experience with AWS data services (Glue, Redshift, S3, Lambda, EMR/Spark, Kinesis, Athena, RDS, API Gateway, etc.).
• Proficient in programming languages such as Python and SQL; experience with Shell scripting and Scala is a plus.
• Strong experience designing, implementing, and managing data lakes, data warehouses, and data ingestion pipelines on AWS.
• Proven experience with ETL/ELT processes, data modeling, and big data frameworks.
• Demonstrated ability to lead, mentor, and coach engineers in a collaborative team environment.
  Experience with DevOps practices, CI/CD pipelines, and infrastructure-as-code tools (e.g., CloudFormation, Terraform).
  Excellent problem-solving, communication, and organizational skills.