Key Responsibilities Design, develop, and optimize data pipelines using Python and AWS services such asGlue, Lambda, S3, EMR, Redshift, Athena, and Kinesis. Implement ETL/ELT processes to extract, transform, and load data from various sources into centralized repositories (e.g., data lakes or data warehouses). Collaborate with cross-functional teams to understand business requirements and translate them into scalable data solutions. Monitor, troubleshoot, and enhance data workflows for performance and cost optimization. Ensure data quality and consistency by implementing validation and governance practices. Work on data security best practices in compliance with organizational policies and regulations. Automate repetitive data engineering tasks using Python scripts and frameworks. Leverage CI/CD pipelines for deployment of data workflows on AWS. Required Skills and Qualifications Professional Experience:5+ years of experiencein data engineering or a related field. Programming: Strong proficiency inPython, with experience in libraries likepandas,pySpark,orboto3. AWS Expertise: Hands-on experience with core AWS services for data engineering, such as: AWS Gluefor ETL/ELT. S3for storage. RedshiftorAthenafor data warehousing and querying. Lambdafor serverless compute. KinesisorSNS/SQSfor data streaming. IAM Rolesfor security. Databases: Proficiency in SQL and experience withrelational(e.g., PostgreSQL, MySQL) andNoSQL(e.g., DynamoDB) databases. Data Processing: Knowledge of big data frameworks (e.g., Hadoop, Spark) is a plus. DevOps: Familiarity with CI/CD pipelines and tools like Jenkins, Git, and CodePipeline. Version Control: Proficient with Git-based workflows. Problem Solving: Excellent analytical and debugging skills. Optional Skills Knowledge ofdata modelinganddata warehouse designprinciples. Experience withdata visualization tools(e.g., Tableau, Power BI). Familiarity with containerization (e.g., Docker) and orchestration (e.g., Kubernetes). Exposure to other programming languages like Scala or Java.
