Data Engineering at Abbott Laboratories
Roles, Comp & Culture
An L6 staff data engineer at Abbott Laboratories sits around $207K total comp from 42 verified salary datapoints. The primary Data Engineering tech consists of AWS, Databricks and dbt, according to current job listings. Abbott Laboratories pays data engineers slightly above other Healthcare companies. Reviews put them at 3.7 on Glassdoor, a little below the middle of the pack. Employee sentiment at Abbott Laboratories reads neutral and employee happiness has held flat over the past year. Layoff risk scores low for the next 30 days. 3 data engineering roles are open right now.
Abbott Laboratories data engineer compensation
Each level's figure is the median of individual Abbott Laboratories 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.
Abbott Laboratories 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.
Abbott's employer bargain is stability traded against ceiling. Pay lands slightly above other Healthcare companies, with $154K at senior and $207K at staff, but the ladder caps at 3 levels, which means the path from mid to staff is short and then stops. The Glassdoor rating of 3.7 puts Abbott a little below the middle of the pack, and sentiment on Blind runs mixed, a pattern that usually reflects decent conditions without strong advocacy. The tension is real: engineers who want to work close to consequential data problems in healthcare find meaningful scope here; engineers chasing career optionality or rapid leveling will run out of runway faster than they expect.
Recent Abbott Laboratories events
Layoffs, leadership changes, and other major moves at the company, with dates.
Abbott is not in growth mode for data engineering right now. 3 open data engineering roles across 3 cities, with Weesp leading, which is a thin bench for a company of this size. no tracked layoffs in the past 12 months, and 2 executive departures over the same window suggest some leadership flux without a broader workforce reduction. The happiness tier sits at neutral and the trend is roughly flat, which matters as a directional signal: teams that have stabilized after a difficult period tend to hire more carefully and retain longer than teams still absorbing change. The 30-day layoff risk is low. Joining in the next 12 months likely means landing in a defined role on an existing team rather than building something from scratch.
- Exec departureApr 2026Leadership change
- Exec departureDec 2025Leadership change
- Exec departureMay 2025Leadership change
Notable company events we track, with dates.
Abbott Laboratories data engineering tech stack
The languages, storage, and processing tools Abbott Laboratories data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
Abbott Laboratories runs data engineering inside one of the most regulated environments in the world: FDA-submission pipelines, clinical trial data, device telemetry from products like continuous glucose monitors, and supply chain feeds spanning manufacturing sites across dozens of countries. The data problems here are less about raw scale and more about chain-of-custody, auditability, and domain correctness. Current listings center on AWS, Databricks and dbt with Python, PySpark and SQL, which points to a team that has moved past spreadsheet-and-SQL heritage and into a modern medallion architecture, but where validation logic and regulatory traceability requirements shape every pipeline decision. A data engineer here is building and owning the pipes that connect product telemetry to clinical evidence and manufacturing operations, not just an internal analytics platform.
SQLAbbott Laboratories data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Own and maintain reference architectures for data platforms, data products, integration, and consumption across analytics and AI use cases.
Design and implement data pipelines for various projects and initiatives.
Lead MES activities relating to expansion, projects and enhancements.
Practice for the Abbott Laboratories loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
The data says Abbott Laboratories actually hires at the mid and senior band, with 22 mid-level and 16 senior reports in the pool. Engineers who thrive here are comfortable inside compliance-heavy environments, know how to document lineage for auditors as a first-class concern, and can adapt general pipeline patterns to domain constraints they did not invent. If you want to move fast, ship experiments weekly, or work in a culture with explicit engineering promotion ladders, this is not the right fit. If you find data problems in diagnostics, device telemetry, or pharmaceutical supply chains genuinely interesting, and you can work patiently inside a large regulated organization, the scope is real. Check the ladder before you go in, and prep the loop with pipeline architecture as your primary focus.
Preparing for the Abbott Laboratories loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Compare Abbott Laboratories with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Abbott Laboratories interview difficulty
- 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
- 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
- 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