Sr. Data Engineer

Robots and Pencils

6d•$119,514 - $164,899•Remote

About The Position

We're looking for a Senior Data Engineer to design, build, and deliver the data and AI infrastructure behind an enterprise agentic AI platform. This role is ideal for an engineer with several years of experience who can confidently own features end-to-end, contribute to architectural decisions, and bring strong technical judgment to the problems they take on. In this role, you will work as a key contributor on a cross-functional team alongside AWS Professional Services, embedded with a large enterprise client. You'll help scale an observability data platform from an initial single-agent pilot to a fully automated, end-to-end operations model, building the data pipelines, observability, and agent-enablement tooling around it. You'll be joining real, in-flight work where reliability, security, and scalability matter. You'll collaborate with experienced engineers to enhance existing systems, deliver new capabilities, and contribute to the technical decisions that shape how the platform scales over time.

Requirements

5+ years professional software or data engineering experience, with 2+ years building data platforms or AI/ML systems in production on AWS
Strong software engineering background (Python and SQL, or similar)
AWS Certified Data Engineer – Associate. Candidates without it are considered if there is a clear, near-term path to earning it
Hands-on experience building data lakes on AWS, including S3, Lake Formation, Glue, Athena, and Iceberg or Parquet, with exposure to streaming on Kinesis or MSK
Experience with observability for distributed and AI systems, including CloudWatch, X-Ray, OpenTelemetry/ADOT, Amazon Managed Service for Prometheus and Grafana, and drift monitoring with SageMaker Model Monitor
Working knowledge of agent enablement on AWS, including Amazon Bedrock Knowledge Bases, Guardrails, RAG patterns, and Bedrock Agents
Experience shipping agentic or ML systems in production environments
Experience with MLOps and LLMOps practices, including evaluation, deployment, and monitoring of models and agents
Experience with infrastructure as code using AWS CDK, and CI/CD for data and AI systems
Understanding of IAM and KMS security, data governance, responsible AI principles, and PII handling
Cost optimization experience across storage, query, compute, and model usage
Experience delivering solutions in a consulting or professional services setting, working alongside client and partner teams
Demonstrable, day-to-day usage and knowledge of AI-forward coding tools such as Claude Code and Cursor
Strong problem-solving skills and the ability to navigate ambiguous technical challenges with sound judgment

Nice To Haves

Experience in regulated enterprise environments such as financial services
Additional AWS certifications such as Machine Learning Engineer – Associate, Security – Specialty, or Solutions Architect – Professional
Prior work with AWS Professional Services or other AWS partner delivery teams
Experience with agent frameworks and orchestration tools, such as Strands Agents or LangGraph
Open-source contributions or deep familiarity with the OpenTelemetry ecosystem

Responsibilities

Design, implement, and deploy data and AI/ML systems on AWS end-to-end, from concept through production, including data lake pipelines, observability instrumentation, and optimization for performance, reliability, and cost
Maintain and evolve platforms in production, monitoring for drift, debugging issues, and driving ongoing improvements to reliability and scalability
Bring an AI-forward coding mindset to your daily work, using tools like Claude and Cursor to ship higher-quality work at pace
Partner closely with AWS Professional Services, client engineering, and data teams to align the work with broader platform and business goals
Translate technical tradeoffs, system behavior, and constraints into terms non-specialists can act on
Participate actively in code reviews and design discussions, raising concerns and offering constructive feedback
Contribute to data and AI architecture decisions with thoughtful perspective on tradeoffs and long-term implications
Take ownership of meaningful work end-to-end, including the unglamorous parts of getting AI into production
Raise the bar on engineering practices through your own work and by supporting more junior engineers when opportunities arise