Data Engineering Lead

Location: Malvern, PA Contract

Summary: We are seeking an experienced Tech lead- Data Engineer (15+ years) with a strong background in Java, AWS, Python, PySpark, and event-driven architectures. You will design and build scalable batch and streaming data pipelines, optimize cloud data platforms, and deliver high-quality, reliable datasets that support analytics, reporting, and machine learning workloads.

Key Responsibilities: Architect, build, and maintain event-driven data pipelines using AWS services such as Kinesis, MSK/Kafka, Lambda, Step Functions, SQS/SNS, and Glue/EMR.Develop ETL/ELT workflows using Python and PySpark, ensuring performance, scalability, and cost efficiency. Implement and optimize Spark-based data transformations, partitioning strategies, and data processing frameworks. Design and manage data lake and warehouse structures using S3, Glue Catalog, Athena, and/or Redshift. Build streaming solutions with checkpointing, stateful transformations, idempotency, and schema evolution. Ensure high standards of data quality, observability, monitoring, and alerting (CloudWatch, Datadog, etc.).Implement data security best practices including IAM, encryption (KMS), networking, and governance. Create reusable frameworks, internal libraries, and CI/CD pipelines for automated deployments. Collaborate with data scientists, analysts, and business teams to deliver well-modeled, reliable datasets. Lead design reviews, mentor junior engineers, and contribute to engineering best practices.

Required Qualifications: 15+ years of professional experience in Data Engineering. Strong expertise in Python and PySpark for large-scale data processing. Advanced hands-on experience with AWS (S3, Glue, EMR, Lambda, Step Functions, Kinesis/MSK, DynamoDB, Athena, Redshift).Deep experience building event-driven and streaming data pipelines. Strong SQL experience for analytical and ETL workloads. Hands-on experience with workflow orchestration tools such as Airflow or Step Functions. Experience with CI/CD, Git, and Infrastructure-as-Code (Terraform or CloudFormation).Strong understanding of distributed systems, Spark performance tuning, data modeling, and cloud cost optimization. Knowledge of data security, encryption, networking, and compliance best practices in cloud environments. Soft SkillsStrong design and architectural understanding. Excellent communication and stakeholder interaction skills. Ability to work in a globally distributed team.