Data Engineering at DataEconomy
Roles, Comp & Culture
An L6 staff data engineer at DataEconomy sits around $184K total comp from 15 salary datapoints. The primary Data Engineering tech consists of Airflow, AWS and Azure, according to current job listings. DataEconomy pays data engineers below other Technology companies. Reviews put them at 4.5 on Glassdoor, among the highest of any company here. Employee sentiment at DataEconomy reads neutral and employee happiness has held flat over the past year. Layoff risk scores low for the next 30 days. 1 data engineering role is open right now.
DataEconomy data engineer compensation
Each level's figure is the median of individual DataEconomy 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.
DataEconomy 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.
A Glassdoor rating of 4.5 is among the highest of the companies we track, and that signal is worth taking seriously for a company this size: small companies get reviewed selectively, so sustained high scores usually mean the day-to-day environment is genuinely decent. The happiness picture is neutral and roughly flat, which is honest for a startup-adjacent shop where the work is meaningful but the pace and uncertainty are real. The tension here is pay. Compensation is below relative to other Technology companies, and with 15 verified data points across a 3-level ladder, the range is fairly narrow. Engineers who come here are trading some market comp for what appears to be a functional, low-drama culture, and that trade works better if you value stability in the team over maximizing your offer.
DataEconomy is not in an expansion phase right now. 1 open data engineering role is a thin signal for a company this size, suggesting the team is largely set rather than actively scaling. Hiring is concentrated in Charlotte, not the home base of Dublin, which may indicate the team is distributed by design or has shifted its geographic center. The low layoff risk reading is a positive for near-term stability, but a company with 51 to 200 employees and a single open data role is not a place to join expecting headcount-driven growth over the next twelve months. Someone joining now should expect to grow by taking on more scope on a flat team, not by moving up through new layers of management.
DataEconomy data engineering tech stack
The languages, storage, and processing tools DataEconomy data engineers actually work with, grouped by what they do. Tailor your system-design answers to this stack.
DataEconomy is a small data-focused technology company, and the engineering work reflects that scale: a compact team where data engineers own broad surface area rather than narrow slices of a larger system. The stack, Airflow, AWS and Azure plus PySpark, Python and SQL, points to cloud-native batch pipelines on AWS and Azure with PySpark doing the heavy transformation work and Airflow handling orchestration. At 51-200 employees, there's no dedicated platform team buffering you from infrastructure decisions, which means the role likely spans pipeline construction, data modeling, and some degree of cloud operations. If the company's core product is data intelligence or analytics infrastructure, then the data engineering function is central to what ships, which can mean real ownership but also means pipeline reliability sits squarely on a small team.
SQLDataEconomy data engineer job openings
A live read on what they are hiring: open roles, recent postings, where, and at what level.
Senior engineers are the sweet spot here: 8 of 15 verified reports sit at that level, and the work profile fits someone who can own a pipeline end to end without much scaffolding around them. If you're a mid-level engineer looking to take on more pipeline architecture responsibility in a quieter environment, the gap between $131K and $153K suggests real upside in staying and leveling. Staff roles exist but are limited, so if principal-track growth is your priority, the ladder tops out early and you'll feel that ceiling. Candidates who prefer big-tech tooling, large data volumes, or a defined PE or SRE layer around their work should pass; DataEconomy asks engineers to be more self-sufficient than that. If the fit is there, the loop emphasis on pipeline architecture means your prep should prioritize design problems over algorithmic ones; check the salary ladder before accepting to make sure the offer lands where the data says it should.
Preparing for the DataEconomy loop
The round-by-round process, example questions, and prep plan are on the interview guide.
Compare DataEconomy with other data engineering employers
How the role, pay, and loop stack up against peer companies.
Prepare at DataEconomy 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