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

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