Data Engineering at Koninklijke Philips NV
Roles, Comp & Culture
An L5 senior data engineer at Koninklijke Philips NV sits around $152K total comp from 19 verified salary datapoints. The ladder runs from about $136K at mid up to $152K. Koninklijke Philips NV pays data engineers in line with other Healthcare companies. Reviews put them at 3.8 on Glassdoor, a little above the middle of the pack. Employee sentiment at Koninklijke Philips NV reads neutral and employee happiness has held flat over the past year. Layoff risk scores low for the next 30 days.
Koninklijke Philips NV data engineer compensation
Each level's figure is the median of individual Koninklijke Philips NV 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.
Koninklijke Philips NV 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.
Practice for the Koninklijke Philips NV loop
Round by round, the problems our model predicts for this company's interview. Rehearse the shapes their panels keep returning to.
Preparing for the Koninklijke Philips NV loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Compare Koninklijke Philips NV with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at Koninklijke Philips NV 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