Job Title :: Technical Project Manager - AWS Location :: Fort Mill, SC/New York, NY/Austin, TX (3 days onsite) Experience: 13+ years Job Type :: Full time

Read on to find out what you will need to succeed in this position, including skills, qualifications, and experience.

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. xsgimln

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

Regards, Manvendra Singh