Technical Project Manager

Role: Technical Project Manager - AWS Location: Fort Mill, SC/New York, NY/Austin, TX Experience: 13+ years Mode: Hybrid (3 days WFO) Duration: Full time About the Role We're looking for a hands-on Technical Lead who lives and breathes AWS data engineering and modern AI. You'll architect, design, and deliver cutting-edge data + AI solutions while guiding a sharp team of engineers. If Glue jobs, PySpark magic, serverless wizardry, Python scripts and AI/ML operationalization excite you—you'll feel right at home. What You'll Own & Lead Architecture & Delivery Drive end-to-end architecture for ingestion, transformation, analytics, and AI-powered data products Set the standards, patterns, and roadmaps that shape our data future Hands-on Engineering Build high-performance ETL/ELT pipelines using AWS Glue, Python, and PySpark Craft serverless data services with Lambda, API Gateway & Step Functions Tune Athena, optimize S3 layouts, and lead complex data migrations like a pro AI/ML Enablement Bring AI into real products: RAG pipelines, embeddings, inference endpoints, and more Partner with Data Scientists & ML Engineers to operationalize models with MLOps best practices Quality, Security & Reliability Champion testing, data quality, observability, and lineage Enforce security-by-design with IAM, KMS, VPC endpoints, masking, and tokenization Leadership & Collaboration Mentor engineers, lead sprints, and elevate the team's technical bar Work closely with Product, Security, and Architecture to turn ideas into reality What We Are Looking For 13+ years in data engineering/backend engineering, including 4+ years leading technical teams and driving architecture decisions Deep, hands-on expertise across AWS Data services & AI: AWS Glue (Jobs, Crawlers, PySpark), Lambda (Python), Athena, S3, Glue Data Catalog Python for data engineering (PySpark) and service development ETL/ELT design patterns, orchestration (Step Functions / Airflow), and dimensional + Lakehouse modeling Data migration strategies, validation frameworks, and rollback planning Data lake architecture: Parquet, partitioning, with familiarity in Iceberg IaC with Terraform / AWS CDK and CI/CD pipelines (CodePipeline, GitHub Actions, Azure DevOps) Hands-on experience with modern AI technologies and emerging AI tooling J-18808-Ljbffr