AWS/Data Engineer
Coral Springs, FL Onsite
Responsibilities
- Design and operate data pipelines on AWS - Glue, Step Functions, Lambda, EventBridge, S3 - feeding the Redshift data warehouse.
- Stand up and operate containerized production pipelines on AWS Fargate / ECS (scheduled ingest or scrape, enrich, deliver) with CloudWatch alarms and health monitoring.
- Design dimensional data models - facts, conformed dimensions, and analytics marts - on Redshift over a zero-ETL replica of the operational source.
- Implement document-extraction pipelines using Amazon Bedrock Data Automation blueprints and deliver structured output to downstream consumers via webhook or queue.
- Build and operate Bedrock Knowledge Bases for retrieval-augmented generation: source preparation, indexing, retrieval evaluation, and cost / latency tuning.
- Build agents using the Strands Agents SDK on Bedrock AgentCore - supervisor / sub-agent topologies, tool definitions, and deployment hardening.
- Build applied-LLM data products for reporting, search, and agentic operations.
- Deploy and operate services on AWS end to end, including internal web apps and demos.
- Build internal tooling and integrations, including MCP servers.
- Prototype and ship new AI-powered applications for advisors and internal teams.
- Maintain operational quality - logs, alarms, runbooks, on-call response, post-incident notes - and continuously monitor quality and compliance with data-privacy regulations.
- Conduct day-to-day development through Claude Code with project-scoped MCP servers, document decisions and architectures in the team wiki.
Competencies - Technical (builds with)
- AWS data and serverless services.
- Amazon Bedrock and AgentCore Runtime.
- Python (primary); SQL (baseline).
- Data warehousing and dimensional modeling.
- Agent and LLM frameworks.
- Infrastructure-as-code.
Competencies - Tooling (works with)
- Claude Code as the primary development surface, including parallel sessions.
- MCP servers (AWS, Excolo, Atlassian) as deterministic tool routing for work that previously spanned terminal, IDE, and browser.
- Comfort treating the model as a collaborator - generating alternatives, having design conversations, validating choices before committing - rather than as autocomplete.
Competencies - General
- Writes documentation a teammate can pick up and act on without a meeting.
- Explains trade-offs in plain language to non-engineers (Marketing, Operations, Finance).
- Strong problem-solving, communication, and collaboration skills.
Requirements
- Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, or a related field. Master's preferred at the Senior level.
- Relevant production experience scaled to level (Data & AI Engineer I / II / Senior).
- Authorization to work in the United States without sponsorship.
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