Job Description
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
