This role is a key part of their Data team, focused on building reliable, scalable solutions that reduce manual work and improve accuracy across the organization.

Summary

The client is seeking an Operations Engineer to join their Data team as a strong mid-level individual contributor. This role is built for someone who can own ambiguous business problems, partner directly with cross-functional stakeholders, and turn tedious, error-prone, time-consuming manual work into elegant, reliable automations. Rather than building models, dashboards, and metrics, the successful candidate will spend their time deeply understanding how work actually gets done across the organization, documenting those processes, and designing and building solutions that meaningfully increase productivity. They will work primarily in SQL, Python and across APIs, scripting, and workflow automation tooling, and they will lean heavily on Claude and other AI tools - not just to move faster, but as core building blocks for the automations they ship. This role is not a people-management role. It is a hands-on IC role for someone who wants to find the most painful manual processes at the client and make them disappear.

Essential Duties And Responsibilities

• Own cross-functional automation projects. Partner with Operations, Member Care, Finance, Product, Engineering and leadership to understand painful manual processes, define requirements, and deliver durable solutions that eliminate or dramatically reduce that work.
• Deeply understand and document existing processes. Sit with stakeholders, map current-state workflows step by step, identify where time is being lost and errors are being introduced, and quantify the cost of the status quo so the right problems get prioritized.
• Design elegant automation solutions. Translate messy, real-world processes into clear, maintainable designs. Identify the highest-leverage opportunities for automation, choose the right approach, and account for edge cases, failure modes, and the humans who will rely on the output.
• Build, test, and maintain automations. Develop, test, document, and maintain models, scripts, integrations, and AI-powered workflows that reliably take over manual work, and keep them running as source systems and requirements change.
• Use AI tools as a core part of how you build. Use Claude and other AI tools to design and build automations - including LLM-powered workflows and agents - while validating outputs, handling errors gracefully, and protecting data quality and trust.
• Translate business ambiguity into solutions. Lead stakeholder discovery, ask sharp questions, clarify tradeoffs, and convert vague "this takes forever and breaks all the time" complaints into practical, well-scoped deliverables.
• Measure and improve impact. Track the time saved, errors avoided, and throughput gained from the automations shipped, and use that to prioritize the next set of opportunities.
• Monitor and troubleshoot automations. Identify when an automation breaks or degrades - due to source system changes, schema changes, API changes, or new edge cases - and fix it quickly and durably.
• Raise the bar for the Data team. Contribute to coding standards, documentation, testing practices, reusable building blocks, and overall trust in the automations the client depends on. Help free up the rest of the team from reactive manual requests.
• Other duties as assigned.

Must-Have Skills And Experience

• Approximately 4-5 years of experience in automation, software engineering, scripting-heavy operations, process improvement, data engineering-adjacent work, or a related field; equivalent demonstrated capability will also be considered.
• Strong Python and SQL skills, including writing clean, maintainable code, working with APIs, handling errors and edge cases, and automating multi-step workflows.
• Experience integrating systems and moving data between them via APIs, webhooks, files, or similar.
• Experience working closely with non-technical stakeholders to understand and document how work actually gets done, and to design solutions that fit real-world processes.
• Experience with insurance, healthcare, benefits, claims, commissions, billing, eligibility, carrier files, CRM, member, or agent data.
• Experience translating business questions and pain points into clear requirements and technical deliverables.
• Ability to own projects independently while keeping stakeholders aligned.
• Strong communication skills with both technical and non-technical audiences.
• Excellent problem-solving skills and a proactive, solution-driven mindset.
• Ability to thrive in a fast-paced, high-growth environment with multiple priorities.

Preferred Skills And Experience

• Hands-on experience building with AI tools - using Claude, ChatGPT, or similar to build LLM-powered workflows, agents, or automations, not just to assist with coding.
• Experience with workflow automation and orchestration tooling (e.g., Zapier, Make, n8n, Workato, Airflow, Prefect) or RPA platforms.
• Familiarity with software engineering best practices such as version control, testing, and code review.
• Hands-on experience with BigQuery or other databases, including enough SQL to read from and write to source systems.
• Experience with Fivetran or similar ELT/ETL tooling.
• Familiarity with data governance, privacy, compliance, or regulated data environments.
• Comfort working with imperfect source data and creating clarity from ambiguity.
• Familiarity with BI tools such as Omni or Power BI is helpful but not important for this role.

What Success Looks Like in the First 90-180 Days

Within the first 90 days, the successful candidate will have built a strong understanding of the client's business model, the teams they support, and the manual processes that are costing them the most time and accuracy. They will be independently documenting workflows and shipping automations that take real, tedious work off people's plates.

Within 180 days, they will be owning cross-functional automation projects from discovery through delivery. Business teams will trust them to understand their processes, design the right solution, build it reliably, and maintain it. The automations they have shipped will have measurably reduced manual effort and errors, and stakeholders will be coming to them with their most painful workflows because they know they will fix them.

They will also be helping the Data team become more scalable by reducing the reactive flood of manual requests, establishing reusable building blocks and standards, and finding practical ways to use AI tools to automate faster without sacrificing reliability.