Last updated: August 14, 2026
Big Data Engineer Staffing
Big data engineer staffing is recruiting that places pre-vetted engineers who build and run large-scale distributed systems, Hadoop, Spark, Kafka, streaming pipelines, and multi-petabyte data lakes, for contract, contract-to-hire, or direct hire roles, with a 17-day average time-to-hire.
Most “data engineer” searches are really warehouse and ELT hires. This page is for the other kind, engineers who run Spark clusters, keep Kafka consumers healthy under load, and manage the data lakes and legacy Hadoop systems a standard warehouse can’t touch.
Big Data Engineer Isn’t Just a Bigger Title
Hiring managers usually use “data engineer” and “big data engineer” as the same job title. On our desk, they’re two different searches with two different pay bands. A data engineer might spend a week wiring up a dbt model against a Snowflake warehouse. A big data engineer is who you call when the warehouse isn’t the problem anymore, when volume, velocity, or the sheer number of moving systems has outgrown what a standard pipeline can handle. That’s a different search. It needs a different recruiter. Inside our broader IT staffing services practice, big data engineering is one of the searches we run most often for growth-stage and enterprise data teams.
The scale shows up in the paycheck too. Glassdoor’s May 2026 data put the average big data engineer salary at $144,399 across more than 1,600 reported roles, with the middle band running $114,567 to $184,132. Built In’s 2026 figures land close behind, a $151,131 average and a $160,000 median, with senior specialists clearing $227,000. Engineers who can run Kafka or Flink in production, and keep a streaming job correct when events arrive late or out of order, sit at the top of that range. Batch-only pipeline work pays less. It just does.
Plenty of the roles we staff aren’t greenfield either. A surprising number of enterprise clients still run production workloads on Hadoop, Hive, and HDFS clusters nobody wants to touch, and the engineers who can keep that infrastructure alive while a company migrates to Databricks or a cloud-native lakehouse earn a real premium for it. According to the BLS 2025 Occupational Outlook Handbook, data science and engineering occupations are projected to grow 36% through 2033, well ahead of the average for all jobs. Demand isn’t slowing down. Neither is the gap between engineers who’ve actually run distributed systems in production and everyone else with “big data” on a resume.
The Big Data Stack We Screen For
Every recruiting firm claims to staff “big data.” Few of them can tell a Spark job from a spark plug. Our screen is specific. We look for real depth in the Hadoop ecosystem, HDFS, MapReduce, Hive, YARN, the stuff most bootcamp grads have never touched. Add Apache Spark for large-scale batch and in-memory processing, plus streaming frameworks like Kafka, Flink, and Kinesis for systems that can’t wait on a nightly batch job. On the modern side, that means Databricks, Delta Lake, and Apache Iceberg for lakehouse architecture, plus the cloud-native big data services (AWS EMR, Google Cloud Dataproc, Azure HDInsight and Synapse) most enterprise teams now run workloads on.
Resumes lie. Constantly. We’ve had candidates list “Spark” who had run a tutorial notebook once and nothing since. So our technical screens go past the keyword. We ask what happens when a job runs fine in dev and falls over at ten times the data volume in production. We ask how someone would design a Kafka consumer group that stays correct when messages arrive twice. Candidates who’ve actually done the work answer in specifics. The ones who haven’t don’t. It shows fast, usually by round two.
If your search is narrower than “big data engineer,” we’ve got dedicated benches for it. Teams hiring specifically for event-driven and streaming platforms should see our Kafka engineer staffing page. If the gap is on the storage and query side, our data warehouse engineer staffing bench covers that. And if you want the full pay breakdown by level, city, and specialization before you write the req, our 2026 big data engineer salary guide has it.
Teams whose Kafka footprint has spread across several squads and now needs a single owner should look at Kafka architect staffing for multi-team estates instead.
What Big Data Engineering Hires Look Like Right Now
Compensation figure from Built In’s 2026 big data engineer salary data. KORE1 metrics reflect the trailing 12 months across IT staffing engagements.
How Companies Bring On Big Data Talent
A Hadoop migration and a permanent platform lead aren’t the same hire. We staff both, along with everything in between.
Contract
Best for a defined migration, a Hadoop-to-cloud lakehouse move, or a streaming pipeline build. Engineers who contribute inside the first week, not the first month.
Contract-to-Hire
See how someone performs on your actual cluster before extending a permanent offer. Common when the role is new or “senior” is still being defined.
Direct Hire
For the engineer who’ll own your data platform for years, not months. We screen hard for judgment and system design, not just tool familiarity.
Project-Based
When the need is a small team, not one hire, to stand up a data lake or migrate off legacy Hadoop infrastructure on a fixed timeline.
Roles We Staff for Big Data Teams
The title on the req is rarely the whole story. Titles lie constantly. Here’s the bench we actually draw from.
- Big Data Engineer
- Hadoop Developer / Administrator
- Spark Engineer
- Streaming / Kafka Engineer
- Data Platform Engineer
- Data Lake / Lakehouse Engineer
- Distributed Systems Engineer
- Cloud Big Data Engineer (EMR, Dataproc, HDInsight)
- ETL Engineer at Scale
- Data Infrastructure / Site Reliability Engineer, Data
How We Fill a Big Data Engineering Search
Cluster and Stack Scoping
We start with what you’re actually running, Hadoop, Spark, Kafka, Databricks, cloud-native, and where the real bottleneck is before we open the search.
Targeted Sourcing
We go straight to engineers with production distributed-systems experience. No posting “big data engineer” and hoping the right resume shows up.
Technical Screening
Real failure scenarios, not keyword checks. A lagging Kafka consumer, a Spark job that runs out of memory at scale. We listen for how they’d actually debug it.
Shortlist and Placement Support
A short list of engineers who’ve done this before, plus support through offer, onboarding, and the first weeks on your cluster.
Not Just Resumes. Real Distributed Systems Experience.
- Recruiters who can tell a Spark tutorial from real production experience
- Screening built around failure scenarios, not resume keywords
- 92% retention rate at 12 months
- 17-day average time to hire
“Our last ‘senior data engineer’ hire from a generalist firm had never touched a cluster bigger than his laptop. That’s the gap we’re built to close.”
— KORE1 Senior Data Recruiter
Common Questions
Related KORE1 Resources
- Big Data Engineer Salary Guide 2026, pay bands by level and city.
- How to Hire a Big Data Engineer in 2026, our full hiring-manager guide.
- Data Engineer Staffing, for modern-stack ELT and warehouse hires.
- Top Tech Recruiting Firms in the US, see how KORE1 stacks up against other technical staffing agencies.
What makes big data engineering different from regular data engineering?
Scale and infrastructure ownership, mainly. A regular data engineer builds pipelines against a managed warehouse like Snowflake or BigQuery. A big data engineer runs the distributed systems underneath it, Hadoop clusters, Spark jobs, Kafka streams, at a volume where standard tooling breaks down.
How much does it cost to hire a big data engineer?
$114,000 to $184,000 covers most of the market, per Glassdoor’s May 2026 data, with senior streaming specialists clearing $227,000 at the top end. Our full salary guide breaks it down by level, city, and specialization if you’re building a budget.
Do you place contract big data engineers, or only permanent hires?
Both, plus contract-to-hire. Contract works well for a defined migration or a streaming build with a clear end date. Contract-to-hire lets you evaluate someone on your actual cluster before committing, while direct hire fits a long-term platform owner who needs to live with the architecture decisions for years, not months. Most clients land on one after a short conversation with us about the real timeline.
Is Hadoop still relevant, or should we only hire for Spark and cloud tools?
Hadoop is far from dead. Not even close. It’s mostly retired from new builds, but it’s very much alive in production at large enterprises, running mission-critical workloads while the company slowly migrates off it, and someone still has to keep that infrastructure healthy in the meantime. We staff for both the legacy Hadoop side and the modern Spark, Databricks, and cloud-native lakehouse side, often on the same team.
How do you screen for real distributed systems experience instead of resume keywords?
We ask candidates to walk through failure scenarios instead of definitions. What happens when a Spark job runs fine in dev and falls over at ten times the volume in production? How would someone design a Kafka consumer that stays correct when messages arrive twice? Candidates who’ve actually run these systems answer in specifics. The ones who haven’t circle back to generalities fast.
Where does KORE1 place big data engineers, and is remote hiring an option?
Nationwide, and yes. We staff big data roles across all 50 states with dedicated coverage in major tech metros, and most current searches include a remote or hybrid option, since distributed-systems talent tends to be distributed geographically too and sourcing isn’t limited to wherever the office happens to be.
Ready to Hire a Big Data Engineer?
Tell us what you’re running today, Hadoop, Spark, Kafka, a lakehouse migration, and we’ll bring you engineers who’ve actually done the work. Most clients see qualified candidates inside 17 days.
