Job Description

- 8+ years of Total work experience with Data Platform covering ETL Pipelines, Data Quality, Data Warehouse Modelling, BI Dashboards and Proficient with designing, coding, optimization & deployment. - 4+ years of hands on end to end project experience in AWS Cloud Native Data Services using Data Lake (S3, Lake Formation, Athena), Processing (Lambda, Python, Glue, Spark, EMR, SQL),  Data Quality (Glue DataBrew), Storage (RDS, Redshift, DynamoDB), Orchestration (Step Functions, EventBridge, Airflow) - Additionally BI Analytics Dashboard experience using Tableau &/ Powe BI with Cloud Native services integration experience. - Additional experience in DataOps (CI CD) will be added advantage.   Key Responsibilities : - Analysis, Design, Development, Enhancements, defect resolution and production support of Big data ETL development using AWS native services. - Create data pipeline architecture by designing and implementing data ingestion solutions. - Integrate data sets using AWS services such as AWS Glue, Lambda functions with Step Functions / Airflow - Design & Develop ETL generic metadata driven framework using Python, SQL, PySpark - Design and optimize data models on AWS Cloud using AWS data stores such as Redshift, RDS, DynamoDB, S3, Athena - Design/Configure Data Lake consumption using Athena or Redshift Spectrum with data residing AWS S3 (Parquet, JSON, CVS) - ETL Job Remediations & monitoring using Scripting & native services like CloudWatch - Tableau/Power BI Dashboard Build & Cloud Integration - Need to have hand on experience in using AWS services by leveraging AWS (API, CLI and SDK) - Need to have end client (onsite/onshore) collaboration experience across teams (Dev, Test, Release, Support) - Good verbal & written communication skill with project lifecycle experience (Agile / Phased)