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Hire Databricks Developers for Lakehouse, Spark and ML Workloads
Pre-vetted Databricks developers for lakehouse builds, PySpark, Delta Lake, Unity Catalog, Lakeflow and MLflow, ready within 48 hours.
Stack we work in
- Databricks
- Apache Spark
- PySpark
- Unity Catalog
- Lakeflow
- Auto Loader
- MLflow
- Python
- Projects delivered
- 680+
- Clients served
- 310+
- Client retention
- 98%
- Certified engineers
- 20+
- Building since
- 2018
Figures as of Oct 2026
Trusted by
- Aralia
- Rosa Pistrina
- Lashbo
- Micromunchi
- Payroll Ninja
- POD
- Petdx
- Yokey
- Spin Up
- Starwear
- Udlins
- Vegpal
- Wander Wings
- Wish It
- Wisl
Why teams hire Databricks developers from us
Our Databricks developers build lakehouses on Delta Lake, write PySpark and Spark SQL jobs, and set up Unity Catalog so access and lineage are governed in one place.
Read the full overview
They build pipelines with Lakeflow and help data scientists track and deploy models with MLflow. They join your workspace, repositories and time zone, starting with a 15-day free trial.
Spark jobs are slow and expensive
Oversized clusters, skewed joins and small files make jobs run long and push compute costs up.
No governance across workspaces
Tables and permissions are spread across workspaces and the Hive metastore, and the Unity Catalog move keeps slipping.
Notebooks never reach production
Useful notebooks stay as experiments because nobody has turned them into scheduled, tested pipelines.
Spark skills are hard to hire
Engineers with production Databricks and Spark experience are scarce, and permanent hiring takes months.
Outcomes
What you get
- Databricks developers contributing within days of selection
- Faster, cheaper Spark jobs
- One governed catalog for tables, permissions and lineage
- Notebooks turned into tested, scheduled pipelines and served models
- Code, models and data that you own, with no lock-in
Roles, projects and skills
Databricks developer roles you can hire
Pick one profile or combine several. Each developer works in your tools, repositories and process.
Databricks data engineer
Builds and runs ingestion and transformation pipelines on the lakehouse.
- PySpark and Spark SQL
- Delta Lake tables
- Lakeflow Declarative Pipelines
- Auto Loader ingestion
Lakehouse architect
Designs a lakehouse that stays governed as more teams use it.
- Medallion architecture
- Unity Catalog setup and migration
- Workspace and environment design
- Delta Sharing
Spark performance engineer
Makes jobs faster and cheaper without changing their results.
- Partitioning and file compaction
- Cluster sizing and Photon
- Skew and shuffle tuning
- Job cost monitoring
ML engineer on Databricks
Takes models from notebook to production on the same platform as the data.
- MLflow tracking and model registry
- Model Serving
- Feature tables
- Batch and streaming scoring
Databricks SQL and analytics engineer
Serves lakehouse data to analysts and BI tools.
- Databricks SQL warehouses
- dbt on Databricks
- Dashboards and alerts
- Power BI connections
What our Databricks developers work on
New lakehouse
Delta Lake storage, medallion layers, Unity Catalog and pipelines set up for a first production workload.
Migration to Databricks
Moving from Hadoop, on-premises Spark or a legacy warehouse, with reconciled data at each step.
Unity Catalog adoption
Moving tables and permissions out of the Hive metastore into governed catalogs with lineage.
Streaming pipelines
Auto Loader and Structured Streaming for near real-time tables and alerts.
Machine learning in production
MLflow tracking, registry and serving so models are versioned, deployed and monitored.
Job and cluster cost tuning
Right-sizing clusters, fixing slow jobs and tracking spend per workload.
Lakehouse and Delta Lake
Reliable tables at any scale.
- Delta Lake tables and schema evolution
- Medallion architecture
- OPTIMIZE, Z-ordering and liquid clustering
- Time travel and table recovery
Built with
- Delta Lake
Pipelines and orchestration
Data that arrives on time.
- Lakeflow Declarative Pipelines
- Lakeflow Jobs
- Auto Loader and Lakeflow Connect
- Spark Structured Streaming
Built with
- Lakeflow
- Auto Loader
Governance and delivery
Access, lineage and repeatable releases.
- Unity Catalog permissions and lineage
- Delta Sharing
- Databricks Asset Bundles
- CI/CD with Git and GitHub Actions
Built with
- Databricks
- Unity Catalog
- Delta Sharing
- Databricks Asset Bundles
Machine learning
Models built and served next to the data.
- MLflow experiment tracking
- Model registry in Unity Catalog
- Model Serving endpoints
- Feature engineering pipelines
Built with
- Unity Catalog
- MLflow
How hiring works
Tech consulting
We review your requirements, platform and goals, and recommend the Databricks developer roles and seniority your project needs.
You receive Role and skills plan
Shortlisting
We shortlist pre-vetted Databricks developers who passed our technical evaluation, communication check and team-fit review, and who match your stack and time zone.
You receive Developer profiles
Your interviews
You interview the shortlisted developers and choose. Nothing starts until you are happy.
You receive Your chosen developers
Onboarding and free trial
Your developers join your tools, repositories and ceremonies within 48 hours of selection. The first 15 days are a free trial.
You receive Developers working in your workflow
Delivery and reporting
The team delivers in agile sprints with daily progress reports, and you scale the team up or down as needs change.
You receive Daily reports and sprint releases
Tech stack
Technologies we use
AI and machine learning
Data platforms
Pipelines and orchestration
- Apache Spark
- PySpark
- Spark SQL
dbt- Apache Kafka
BI and analytics
Power BI
Languages
Cloud and DevOps
Other tools
Hiring models
Choose how you hire
Pick the commitment that fits your roadmap and change it as your needs change. Every model works in your time zone, with clear pricing agreed after a call and no hidden costs.
Full-time dedicated developer
A Databricks developer who works only on your project, full working days, as part of your team. Billed monthly, with a 15-day free trial.
Part-time developer
Half-day availability for steady Databricks developer work that does not need a full seat. Billed monthly, and you can move to full-time at any time.
Hourly
Pay for the hours used, for fixes, audits, small enhancements and short spikes of work. Hours are reported daily, with no long-term commitment.
Project-based engagement
A fixed scope, timeline and price, delivered by a team we manage end to end, with milestone payments.
Dedicated team
A cross-functional squad of developers, QA, a designer and a project manager or scrum master, sized to your roadmap, with one point of contact.
More roles
Need other skills on the same team?
Hire data, AI, web and mobile developers alongside your Databricks developers, or build a dedicated team around your roadmap.
Free trial
Try first, decide later with a 15-day free trial
Interview the shortlisted developers, then judge the work in your real project for 15 days before you commit.
Questions, answered
It depends on the seniority, the platforms involved and the hiring model: full-time, part-time, hourly or project-based. After a short call we share profiles and a clear monthly or hourly rate, with no hidden costs, and the first 15 days are a free trial. As a guide, our data and MLOps engineers range from $30 to $70 per hour (USD), depending on seniority and certifications, and full-time monthly engagements cost less per hour than hourly work. We confirm your exact rate after a short call.
It depends on the role, seniority and hiring model. Full-time and part-time developers are billed monthly, hourly work is billed for the hours used, and project-based work is paid in milestones. After a short call we share developer profiles and a clear monthly or hourly rate, with no hidden costs.
Pre-vetted developers can join your team within 48 hours of you selecting them.
Yes. We align developers to your preferred time zone and working hours so you can collaborate in real time.
The first 15 days are free. Use them to assess the developer's skills, code quality, communication, delivery and fit with your process. If you are satisfied, you continue; if not, you can stop with no obligation.
We sign an NDA, use data encryption, secure storage and regular security audits, and follow GDPR requirements. Equitysoft is ISO 27001 certified. You own all code, data and IP, with no lock-in.
Tell us and we will replace them quickly, so your project stays on track.
You set the priorities and direct the work, just as you would with your own staff. You get daily progress reports and talk to the developer through your usual email, video, chat and project tools. We take care of employment, equipment, attendance, performance support and replacements.
Each candidate passes a technical evaluation with practical tasks and code review on the stack you need, such as PySpark, Delta Lake, Databricks SQL and MLflow, not just a CV. We also check spoken and written English, how clearly they explain technical decisions, and their experience with agile ceremonies, code review and documentation. You then interview the shortlist and choose.
Yes. They inventory your existing tables, jobs and permissions, plan the catalog and schema layout, migrate in stages, and update jobs and notebooks so nothing breaks along the way.
Yes. They set up MLflow tracking and the model registry, build feature pipelines and deploy models to Model Serving or batch scoring jobs, working with your data scientists or ours.
Tell us about the Databricks developer you need
Share your workspace setup, workloads and goals. We will propose pre-vetted Databricks developers you can start trialing within 48 hours.