Cotiviti Data Engineer Interview Guide

SQL is where this loop concentrates, and the signal Cotiviti is reading for is not just whether you can write a correct query but whether you reason about claims-shaped data problems. Think window functions applied to adjudication sequences, aggregation across member-plan-provider hierarchies, and deduplication logic on transaction records that may arrive from multiple source systems with overlapping keys. A strong answer here names the edge case before the interviewer does: what happens when a claim is reprocessed, when member IDs collide across payer feeds, when a date field carries multiple interpretations depending on claim type. Generic SQL proficiency passes at other companies; at Cotiviti, grounding your answers in the transaction-and-member data model that payment integrity runs on is what separates a hire from a pass.

The failure mode that shows up in Cotiviti loops is engineers who are technically correct but contextually thin. If you solve the SQL problem and then describe the pipeline you'd build without mentioning data contracts, client schema variability, or downstream audit requirements, you've signaled that you haven't thought about where this data goes or who depends on it. The inverse of that, what reads as a hire, is an engineer who volunteers the quality and lineage considerations unprompted. A 3.4 Glassdoor rating and mixed Blind sentiment suggest internal frustration around process and pace, and some of that bleeds into interviews: interviewers here tend to probe whether you can stay productive in a slow-moving client-driven environment. Candidates who frame every previous job as 'I moved fast and shipped' without acknowledging constraint management often don't land.

Cotiviti's loop is filtering for engineers who can operate inside a compliance-first data environment without needing someone to define the guardrails for them. The business lives on catching payment errors in claims data before money moves, which means the engineering work has real downstream consequences: a broken pipeline or a miscounted member record touches audits, client SLAs, and in some cases CMS reporting. What the interviewers are trying to establish is whether you understand that context and adjust your instincts accordingly. That shows up in how you talk about data quality, how you describe handling schema drift from client source systems, and whether you treat HIPAA-adjacent data handling as an afterthought or a design input. The filter is less about raw technical speed and more about judgment under constraint.

Given that SQL dominates the loop, SQL prep is where the first hours go. Focus on healthcare-adjacent problem shapes: claims aggregation, member deduplication, time-series logic across adjudication windows. After that, prep your data quality and pipeline reliability stories, specifically ones where you caught a schema issue or upstream feed problem before it reached a downstream consumer. That narrative matters more here than any infrastructure or streaming work you've done. With only 2 levels on the ladder, leveling stakes are relatively contained, but the gap between mid and senior at Cotiviti is largely about domain fluency and stakeholder communication, so senior candidates should have a clear example of translating messy source data into something a non-engineering stakeholder could act on. Skip deep prep on orchestration tooling or streaming architecture; SQL is where the interview time actually goes.