Somebody always notices first when a pipeline breaks. Usually it's not an alert — it's a person in Slack asking why yesterday's revenue number doesn't match what's on the dashboard right now. Whoever quietly fixes that before it turns into a bigger problem is basically doing this job. Title's
Remote Data Engineer (SQL Focus). Pay is
$153,500 per year; fully remote; open to people in the US, Canada, the UK, the EU, Australia, India, and a handful of other places.
Why This Seat Exists
Last year, the pipelines this team runs processed more than 7 billion transactions and reduced data delivery times by 43%. Numbers like that don't hold steady on their own — someone has to keep watching query performance, catch a broken join before it wrecks a dashboard, and rebuild things as volume keeps climbing. That's this role, not some side responsibility tacked onto a bigger job title.
What Fills Your Week
Some days it's writing new SQL pipelines for a batch job that's been running too slow. Other days it's tracing why a dashboard shows the wrong total, which usually means digging through three layers of transformations to find where something silently broke. There's also the less exciting stuff — tuning indexes, documenting how a pipeline actually works so the next person doesn't have to reverse-engineer it, coordinating with a BI team who just needs clean numbers and doesn't care how you got there.
Roughly, that breaks down to:
- Building and maintaining SQL pipelines for both real-time and batch work
- Connecting data from APIs, cloud storage, and the occasional legacy database nobody wants to touch
- Tuning performance — indexing, partitioning, query rewrites
- Working with BI teams so their dashboards pull data that's actually trustworthy
- Automating ETL work through tools like Apache Airflow or Talend
- Keeping data governance and security standards intact, not just on paper
- Chasing down bottlenecks with existing monitoring tools
- Writing documentation clear enough that someone new can follow it without asking you first
What Gets You Considered
A Bachelor's degree covers the education requirement. Past that, you need 3 or more years actually building and running data pipelines, with SQL as a real strength, not a bullet point. You should have worked hands-on with at least one major cloud warehouse — Snowflake, Redshift, BigQuery, doesn't matter which — and be comfortable with ETL frameworks and orchestration tools without needing a tutorial. Python scripting helps a lot, since most transformation work eventually leans on it.
Two things matter more than they show up on paper: catching a data quality problem before it reaches a report and explaining a technical fix to someone who isn't technical without losing what actually matters in the explanation. Both come up constantly. Neither is easy to teach.
This posting also runs on
Naukri Mitra, and applications from there go through the same process as anywhere else.
What You'd Actually Touch Day to Day
Snowflake, Redshift, or BigQuery for warehousing. PostgreSQL, SQL Server, or MySQL underneath. Airflow, DBT, or Talend for orchestration. Spark and Hadoop are used for heavier workflows. GitHub Actions and Jenkins handle CI/CD. Python, Shell, and Bash for scripting. Power BI, Tableau, and Looker sit atop the reporting stack.
What This Work Has Actually Produced
Data lag on key analytics workflows dropped by 35%. Uptime's sitting at 99.98%. Five product launches relied on this team's reporting pipelines, and none hit a wall. That's not luck — it's what happens when engineers treat a pipeline as something to keep tending, not something you build once and forget about.
How the Team Actually Runs
It's remote enough that your schedule is mostly your own — plenty of people here work whatever hours they think they work best in. Stand-ups happen daily but stay short. Slack covers most of the smaller questions that don't need a full call. Twice a year there's a hackathon, and more than a few real pipeline improvements have come out of those sessions rather than a planned sprint.
Pay and What Comes With It
- Fully remote, flexible scheduling
- Health coverage for you and your dependents
- Paid time off, kept separate from sick leave
- A learning budget for platforms like Udemy, Coursera, and cloud certifications
- An annual bonus tied to performance
Where People Tend to Go From Here
Engineers who stick around end up leading larger infrastructure projects, mentoring people who are new to the team, or having real input on technical standards. There's also room to get pulled into machine learning or predictive analytics work if that's a direction that interests you — it's not required, but the door's open.
What Success Actually Looks Like
Pipelines nobody has to babysit. Dashboards a BI team trusts without double-checking the math. Documentation solid enough that a new hire can follow it without pinging you every hour. None of it looks impressive from the outside, but it's the difference between a data team people rely on and one they route around.
Applying
Send a resume, and include a GitHub profile or specific project examples if you've got them — actual pipelines you've built say more than a bullet-point skills list ever will. Review happens as applications come in. Anyone who moves forward gets a technical interview covering SQL, pipeline design, and some real troubleshooting, not just theory.
