Lead AWS Data Engineer Data Lakes, Pipelines & Cloud

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. #J-18808-Ljbffr