Senior Data / ML Engineer

About the Role:

We are seeking a highly skilled Senior Data / ML Engineer to join our elite Data Science & Engineering team. In this pivotal role, you will design, build, and maintain robust, scalable data pipelines and infrastructure on AWS, powering critical analytics, machine learning models, and business reporting.

Your Impact:

• Collaborate with cross-functional teams including data scientists, analysts, and product owners to ensure reliable, performant data delivery across the organization.
• Accelerate high-quality development using modern practices: AI-assisted coding, containerization, and Infrastructure as Code (IaC).

Key Responsibilities:

• Pipeline Architecture: Design, develop, and optimize large-scale ETL/ELT pipelines leveraging AWS EMR (Spark), Glue, Lambda, and Step Functions.
• Orchestration: Manage complex data workflows using Apache Airflow (Amazon MWAA or self-managed) with a focus on reliability, observability, and SLA adherence.
• Data Storage Solutions: Architect storage across Amazon S3 (data lake), Redshift (warehouse), and RDS (relational databases). Apply best practices for partitioning, compression, and cost optimization to maximize efficiency.
• Containerization & Microservices: Build and maintain containerized applications using Docker and ECS/Fargate, including CI/CD automation with GitHub Actions.
• Serverless Solutions: Develop event-driven processing solutions utilizing Lambda, SQS, SNS, and EventBridge.
• AIDevOps: Leverage AI-powered coding assistants (e.g., Cursor, Kiro, Claude Code) to accelerate development, enhance code reviews, and automate documentation.
• Data Quality & Governance: Implement frameworks for monitoring, alerting, and enforcing data integrity. Define security policies aligned with regulatory standards.
• ML Ops Integration: Collaborate closely with Data Scientists to productionize ML models and feature pipelines using SageMaker.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, or related fields.
• Experience: 5+ years in data engineering (with at least 3 years of hands-on AWS production workloads).
• AWS Expertise: Deep proficiency with EMR/Spark, Apache Airflow, S3, Redshift, RDS, Lambda, SageMaker, and ECS.
• Docker & Containers: Solid experience in building, optimizing, and deploying containers; familiarity with orchestration tools like Kubernetes/ECS is a plus.
• Languages: Strong proficiency in Python and SQL (Typescript exposure valued).
• AIDevOps Skills: Demonstrated ability to use AI-assisted development tools for rapid prototyping, code generation, testing, and debugging within modern IDEs.
• Certifications & IaC: AWS certifications (Data Analytics or Solutions Architect) are a plus. Experience with Terraform, CloudFormation, or CDK is required.