Python · SQL · Spark · dbt
Hire data engineers
Vetted data engineers who build reliable pipelines, warehouses and lakehouses, so your analytics and AI work from clean, timely data. Matched to your stack and budget, on your team in 2–3 weeks.
Rated 5.0 on Clutch · CVs in 3–5 days · No long-term lock-in
- Python
- SQL
- Spark
- Airflow
- dbt
- Snowflake
What does a data engineer do?
A data engineer builds and runs the systems that move data from source applications into a warehouse or lakehouse where it can be analysed: ingestion, ETL/ELT pipelines, data models and orchestration. Good data engineering keeps data accurate, up to date and affordable to store and query, so reports, dashboards and AI models can be trusted.
DevKit data engineers work in Python and SQL with Spark, Airflow, dbt and Kafka, on platforms such as Snowflake, BigQuery and Databricks. Every one of them passes a technical interview with our senior engineers before you see their CV.
When to hire one
- Building a data warehouse or lakehouse from scratch
- Creating and maintaining ETL/ELT pipelines from many data sources
- Preparing clean, well-modelled data for analytics, BI and AI
- Fixing slow, fragile or expensive existing pipelines
What our data engineers do
Typical work our data engineers take on for clients.
Data pipelines and ETL/ELT
Batch and incremental pipelines that load data from databases, APIs and files reliably and on schedule.
Data warehouses and lakehouses
Warehouse and lakehouse design on Snowflake, BigQuery, Databricks or Redshift, sized to your workload.
Data modelling and transformation
Clean, documented data models built and tested with dbt and SQL, ready for analysts and BI tools.
Streaming and real-time data
Event streams and near-real-time processing with Kafka, Spark Structured Streaming and cloud services.
Orchestration and data quality
Workflows in Airflow or Dagster with tests, alerts and lineage, so broken data is caught before it reaches reports.
Performance and cost optimisation
Query tuning, partitioning and storage choices that keep pipelines fast and cloud bills under control.
Data engineering technologies we cover
Languages and processing
- Python
- SQL
- Scala
- Apache Spark
- PySpark
- pandas
Orchestration and transformation
- Airflow
- dbt
- Dagster
- Fivetran
- Airbyte
Warehouses and lakehouses
- Snowflake
- BigQuery
- Databricks
- Amazon Redshift
- PostgreSQL
Streaming and cloud
- Kafka
- AWS
- Google Cloud
- Azure
- Docker
Matched to your budget: which level do you need?
We match the level to the work, so you don't pay senior rates for tasks a mid-level engineer handles well, or put critical work on the wrong hire.
Junior
Well-defined tasks with guidance
Bug fixes, tests and smaller features in an established codebase, with a senior on your team to review their work. The most budget-friendly option.
Mid-level
Independent feature work
Builds features end to end in an existing architecture with little supervision. The best value for most product teams.
Senior
Complex features and ownership
Owns complex parts of the system, makes sound technical decisions and mentors others. Worth it where mistakes are expensive.
Tech lead / Architect
Architecture and team leadership
Designs the system, sets standards and leads other engineers. Ideal for new products, rewrites or a team without technical leadership.
Not sure? Tell us the work and your budget on a free call, and we'll recommend the right level for each role.
What's in every profile we send
Within 3–5 business days of our call, you get a shortlist of vetted candidates. Every profile gives you what you need to decide quickly.
- 01
Skills verified by engineers
Each candidate passes a technical interview with our senior engineers. You see what they can actually do, not just what their CV says.
- 02
Interviewer's notes
Strengths, gaps and how the candidate approached the interview tasks, written by the engineer who interviewed them.
- 03
Why this match
How the person fits your role, your team and your budget, and which level we recommend for the work.
- 04
Level and relevant experience
From junior to tech lead, with the projects and domains that matter for your role.
- 05
English, time zone and availability
Communication level, working-hours overlap with your team and the earliest start date.
- 06
A clear monthly rate
One price per engineer per month, so you can compare candidates against your budget straight away.
Client reviews: rated 5.0 on Clutch
Verified Clutch reviews from engineering leaders and founders who scaled their teams with DevKit.
Head of Engineering, IT Company
“Their flexibility and ability to provide access to talented specialists.”
Read on ClutchLead Frontend Engineer, Ultra Commerce
“We were particularly impressed by their management approach.”
Read on ClutchSenior Unity Engineer, Software Company
“Their management side always cared about the engineers provided.”
Read on ClutchCEO, Software & AI Company
“Their responsiveness and the quality of the engineers were impressive.”
Read on ClutchDevOps Engineer, Ubiqube
“They were always ahead of schedule and suggested a lot of modern solutions.”
Read on ClutchSenior Software Engineer, HealthTech Firm
“They combine technical expertise with a strong understanding of business objectives.”
Read on ClutchTeam Lead, AI Solutions Co
“DevKit has found an approach that works well with startups.”
Read on ClutchThree ways to hire
Choose how you want to work. You can switch models as your needs change.
Hiring data engineers: FAQ
How quickly can I hire a data engineer through DevKit?
You usually receive vetted data engineering CVs within 3–5 business days, and the engineer typically starts within 2–3 weeks of the first call.
Which data engineering tools do your engineers use?
Mainly Python and SQL with Apache Spark, Airflow, dbt and Kafka, on Snowflake, BigQuery, Databricks or Redshift, running on AWS, Google Cloud or Azure.
Do I need a senior data engineer?
Not always. If your data platform is in place and someone senior reviews the work, a mid-level engineer often delivers the same results for less, for example adding new sources or models. To design a new warehouse or lakehouse, choose the platform or fix serious reliability and cost problems, a senior engineer or tech lead is usually worth it. We'll recommend the right level on a free call.
How much does it cost to hire a data engineer?
Staff augmentation is billed as a monthly rate per engineer, depending on the level and stack. Tell us your budget and we'll match data engineers to it; exact rates come with the first CVs.
Need engineers next month? Let's talk this week.
Send us the role. Get CVs in 3–5 days.
Tell us what you need. We reply within one business day.
What happens next
- 1
We reply within one business day
A real person reads every request, not a bot.
- 2
A free 30-minute call
We talk through the role, your team, the timeline and the budget.
- 3
Vetted CVs in 3–5 days
Each profile comes with interview notes and a clear monthly rate.
“Their responsiveness and the quality of the engineers were impressive.”
hi@devkit.agency