Data Engineering at Cotiviti

Roles, Comp & Culture

An L5 senior data engineer at Cotiviti sits around $149K total comp from 6 verified salary datapoints. The ladder runs from about $148K at mid up to $149K. Cotiviti pays data engineers in line with other Healthcare companies. Reviews put them at 3.4 on Glassdoor, toward the bottom of the pack. Employee sentiment at Cotiviti reads neutral and employee happiness is trending up over the past year. Layoff risk scores low for the next 30 days.

Last updated: Proudly published by: Jeff Wahl

Cotiviti data engineer compensation

Each level's figure is the median of individual Cotiviti offers at that level, so it reflects a typical outcome rather than an average pulled up by a few large packages. Total comp counts base salary plus equity and bonus annualized over the vest, and the range shown is the middle half of offers, with the top and bottom quarters trimmed off.

L4Mid$148Kmedian
Base$142KRange$147K–$153KReports4 · 2-5 yrsCotiviti loop
L5Senior$149Kmedian
Base$145KRange$145K–$152KReports4 · 5-10 yrsCotiviti loop
Updated 6 verified salary reports + 2 salaries adjusted to total comp

Cotiviti employee sentiment, tracked weekly

Employee happiness for data engineers over the past year, so you can see which direction it is moving, not just where it sits today.

The bargain

The honest read on Cotiviti as an employer sits somewhere between 'stable contract shop' and 'unremarkable place to grind.' A 3.4 Glassdoor rating toward the bottom of the pack is a real signal, and Blind sentiment is mixed, which usually means engineers find parts of the work tolerable and parts frustrating. Pay at $149K for senior sits in line with other Healthcare companies, so you're not trading comp for mission. The happiness picture is neutral and trending up, which is encouraging if you're weighing a move now. The tension here is that healthcare IT tends toward slow cycles, legacy systems, and client-driven timelines. If you're drawn to technical autonomy or fast iteration, those expectations collide with the realities of a regulated payer-services environment.

Neutraltrending up over the past year
20252026
Updated Cotiviti employee happiness
Trajectory

Hiring volume is the clearest indicator of direction, and right now Cotiviti shows no open data engineering roles. That's a contraction signal, not a growth one. The 2-level salary ladder with only 8 verified reports suggests the data engineering function is not a headcount priority at the moment. Companies in the payment integrity space have faced pressure from payer cost-cutting and CMS policy shifts, and Cotiviti has not been immune. The low 30-day layoff risk is reassuring near-term, but a frozen hiring posture heading into the back half of 2026 means anyone joining now should expect limited upward mobility until headcount expands again. Watch for whether DE listings reopen before committing.

Prepare for the interview
01 / Open invite
02min.

Walk into Cotiviti knowing the SQL pattern they'll test.

a Cotiviti SQL query, the same shape a screen would give you.
The diff against expected. Where ties broke. What you missed.
sandbox
1SELECT user_id,
2 COUNT(*) AS sessions
3FROM events
4WHERE ts >= NOW() - INTERVAL '7 day'
5
Execute your solution0.4s avg.
GoogleInterview question
Solve a Cotiviti problem
The work

Cotiviti's core data problem is healthcare claims: the company processes billions of dollars in medical and pharmacy claims annually for health plans, identifying payment errors, fraud, and waste before money leaves the door. That work generates transaction-level data at volume, with real regulatory surface (HIPAA, CMS audit requirements) and latency constraints tied to claims adjudication cycles. A data engineer here is likely building pipelines that feed payment integrity models, member analytics, or provider network data products. The stack runs on whatever the health plan clients already use, which means adapting to diverse source schemas and ingestion patterns rather than owning a single clean warehouse. The job shape is closer to data plumbing and quality enforcement than greenfield platform building.

Unreviewed Models

> Return ml_models rows whose mdl_name never appears as tbl_name in dq_checks. Output the mdl_name, version, framework, and status for each such model, sorted by train_at descending.

Who should pursue it

The salary data skews senior-band heavy, with 4 mid-level and 4 senior reports clustered within a $4K range, suggesting Cotiviti doesn't operate a wide career ladder for data engineers. Engineers who do well here tend to be comfortable with healthcare domain complexity, patient with stakeholder-driven timelines, and skilled at translating messy claims data into defensible analytical outputs. SQL proficiency matters; SQL is what the interview covers, which reflects the job. If you want to build streaming infrastructure, own a modern platform end to end, or move fast on greenfield tooling, this is probably not the right landing spot. If you're an experienced mid or senior engineer who wants reliable work in a regulated domain with manageable stress, it's worth a look. Check the salary ladder before you engage, and prep the SQL loop thoroughly.

Preparing for the Cotiviti loop

The round-by-round process, example questions, and prep plan are on the interview guide.

Compare Cotiviti with other data engineering employers

How the role, pay, and loop stack up against peer companies.

02 / Why practice

Prepare at Cotiviti interview difficulty

  1. 01

    Reading a solution is not the same as writing one

    Every engineer who has frozen on a query they had read a dozen times knows the gap. The only preparation that closes it is producing the answer yourself, under time, before the interview does it for you

  2. 02

    76% of hiring managers reject on the coding task, not the resume

    From HackerRank's 2024 Developer Skills Report. Candidates who look strong on paper still fail the live screen if they haven't done timed, executable practice

  3. 03

    5 problem shapes cover 80% of data engineer loops

    Dedup, sessionization, top-N-per-group, slowly-changing dimensions, partition tricks. Writing the shapes by hand turns the unfamiliar into pattern recognition

Related Guides