A Data Engineer designs, builds and manages the information or big data infrastructure. Develops the architecture that helps analyze and process data in the way the organization needs it. Makes sure those systems are performing smoothly.
Contract Job Summary We are seeking an experienced Data Engineer with strong expertise in Python, PySpark, AWS Glue, and AWS data services to build and support scalable ETL/ELT pipelines for enterprise financial and data platforms. The ideal candidate will have hands-on experience with cloud-based data lakes, distributed data processing, CI/CD, and production support. Key Responsibilities Design, develop, and optimize ETL/ELT pipelines using Python, PySpark, and AWS Glue . Build scalable data ingestion and transformation solutions for enterprise data platforms. Develop cloud-native data lake solutions and ensure data quality and reliability. Implement CI/CD pipelines using GitHub Actions . Support production environments, troubleshoot pipeline issues, and optimize performance. Collaborate with business and technical teams in an Agile environment. Required Qualifications Bachelor's degree in Computer Science or related field. 7+ years of Data Engineering experience. Strong experience with Python, SQL, Apache Spark (PySpark), and AWS Glue . Hands-on experience with AWS S3, Athena, Lambda, Step Functions, and EventBridge . Experience building enterprise ETL/ELT pipelines and cloud data platforms. Knowledge of CloudFormation or Terraform , Git, and GitHub Actions. Strong understanding of data modeling and distributed data processing. Excellent communication and problem-solving skills. Preferred Skills Financial Services or Asset Management experience. Snowflake or Databricks. Apache Iceberg, Delta Lake, or Hudi. Airflow, Kafka, or Kinesis. AWS Certifications. This role is ideal for a Data Engineer with strong AWS and Spark expertise who enjoys building scalable, high-performance data solutions in a cloud environment.
