Convergehealth - Data Operations Engineer, Expert Services-innovation_delivery_transformation

Deloitte Deloitte · Consulting

Data Operations Engineer on Converge for Healthcare's Expert Services team, responsible for designing and operating cloud-native data pipelines that turn healthcare data into decision-ready analytics. This role involves data integration, validation, profiling, quality assurance, and enabling analytics through BI dashboards and ML Lab workflows. The engineer will also focus on automation, orchestration, and collaborating on product evolution, using emerging AI tooling and LLM-enabled data exploration.

What you'd actually do

  1. Design, build, and optimize cloud-native ETL/ELT pipelines that ingest client source data and conform it to the Data Studio platform's foundational data model — making real-world healthcare data ready to power production analytics.
  2. Profile, validate, and QA large, complex healthcare datasets for accuracy, completeness, and conformance to platform standards; combine traditional debugging with LLM-enabled data exploration and ML-based anomaly detection to find and resolve issues faster than manual approaches allow, partnering with client and Deloitte teams as needed when integration issues require it.
  3. Develop the analytics layer of the Data Studio platform — including BI dashboards, self-service reporting, and ML Lab workflows — putting validated, production-ready data in the hands of consulting teams and clients.
  4. Implement and maintain workflow automation, monitoring, and alerting using event-driven architectures and orchestration tools, with the goal of building systems that run reliably without constant intervention.
  5. Act as a hands-on technical voice into the Data Studio platform's evolution — translating real-world delivery learnings into concrete product, data model, and platform enhancement opportunities, and partnering with product and engineering teams to validate and pressure-test new capabilities before they ship.

Skills

Required

  • Expert SQL proficiency, including complex query authoring, data profiling, performance tuning, and query optimization across large-scale, messy datasets
  • Strong Python proficiency for data wrangling, scripting, automation, and integrating ML/AI capabilities into data pipelines
  • Hands-on experience designing and operating cloud-native data pipelines, with judgment around when to use which tool and how to debug distributed systems when things break
  • Sound data modeling judgment, including conforming heterogeneous source data to standardized analytics models without losing fidelity
  • Demonstrated experience working with large, complex datasets across structured, semi-structured, and unstructured formats
  • Forward-thinking engineering mindset, including fluency with modern code collaboration workflows (Git, pull requests, code review)
  • Strong ownership mindset and comfort with ambiguity — able to self-manage priorities, juggle concurrent workstreams, and adapt as priorities shift
  • Clear communicator who works well across distributed engineering, product, and occasional client or consulting stakeholders, including across international time zones

Nice to have

  • practical familiarity with AWS data services (e.g., Redshift, Glue, S3, Step Functions, Lambda)
  • exposure to AWS AI/ML services (e.g., Bedrock, SageMaker)
  • practical use of AI-assisted development tools (e.g., Claude Code, GitHub Copilot)
  • curiosity about emerging AI/ML techniques such as agentic patterns, RAG, and vector databases
  • Working familiarity with modern BI tools (e.g., Tableau, Power BI, Superset)
  • Working familiarity with workflow orchestration platforms (e.g., Airflow, Step Functions)

What the JD emphasized

  • hands-on technical role
  • designing and operating the cloud-native data pipelines
  • applied AI tooling
  • LLM-enabled data exploration
  • ML-based anomaly detection
  • AWS data services
  • AWS AI/ML services
  • agentic patterns, RAG, and vector databases
  • AI-assisted development tools

Other signals

  • applied AI tooling
  • LLM-enabled data exploration
  • ML-based anomaly detection
  • AI/ML services (e.g., Bedrock, SageMaker)
  • agentic patterns, RAG, and vector databases