Hire Pre-Qualified Databricks Engineers and Architects
Databricks has become the default lakehouse platform for data-intensive organizations. But engineers who understand Unity Catalog governance, Delta Lake optimization, and Spark tuning at scale are in critically short supply. We source them from 200+ curated delivery partners.
Why Databricks talent is scarce — and getting scarcer
Databricks has evolved from a Spark management layer into a full lakehouse platform with Delta Lake, Unity Catalog, Databricks SQL, MLflow, and an expanding AI/ML ecosystem. The platform's growth has outpaced the talent market by a wide margin. Companies committed to Databricks as their primary data platform are competing for the same small pool of engineers who understand both the data engineering and ML sides of the lakehouse architecture.
The specificity problem is real. A "Spark developer" from 2020 doesn't automatically understand Delta Lake's transaction log, Unity Catalog's three-level namespace, or how to build medallion architectures that balance performance with governance. The platform has changed dramatically, and the most valuable Databricks engineers are the ones who've kept up.
The gap is especially acute for Unity Catalog specialists, Delta Live Tables developers, and engineers with multi-cloud Databricks experience. Xylity sources these niche profiles from partners who specialize in modern lakehouse platforms.
The cloud dimension adds another layer of complexity. Azure Databricks, Databricks on AWS, and Databricks on GCP each have platform-specific nuances — different network configurations, identity management approaches, and cost optimization strategies. Our matching process evaluates candidates against your specific cloud environment, not just "Databricks" generically.
Databricks specialists ready for your projects
Every profile is evaluated through scenario-based assessment specific to Databricks workloads and your cloud platform.
Databricks Platform Architect
Designs lakehouse architecture, Unity Catalog governance frameworks, workspace topology, and multi-cloud strategy. Leads platform-level decisions for enterprise Databricks deployments.
Databricks Data Engineer
Builds and maintains medallion architecture pipelines using Delta Live Tables, Auto Loader, and structured streaming. Implements data quality checks, schema evolution, and performance optimization.
Databricks ML Engineer
Develops and deploys ML models on Databricks using MLflow, Feature Store, and Model Serving. Builds end-to-end ML pipelines from feature engineering through production inference.
Databricks SQL Analyst
Builds Databricks SQL dashboards, optimizes SQL warehouse performance, creates data products, and implements BI connectivity. Bridges the gap between data engineering and business analytics.
Databricks Migration Engineer
Migrates workloads from legacy platforms — Hadoop, on-prem Spark, AWS EMR, Azure HDInsight — to Databricks. Handles job conversion, performance benchmarking, and parallel validation.
Unity Catalog Specialist
Implements Unity Catalog governance: metastore configuration, data access policies, audit logging, lineage tracking, and cross-workspace data sharing. Critical for enterprise Databricks governance.
Healthcare analytics firm needed 3 Databricks engineers for a lakehouse buildout — and couldn't find a single qualified candidate in 6 weeks.
An IT services partner specializing in healthcare analytics had won a lakehouse modernization project — replacing a legacy Hadoop cluster with Databricks on Azure. The project required 3 Databricks data engineers with specific experience in Delta Live Tables, Unity Catalog governance, and HIPAA-compliant data handling.
After 6 weeks with two different staffing agencies, they had interviewed 8 candidates. None had production Delta Live Tables experience. Most were generic Spark developers who had completed Databricks Academy courses but had never implemented a medallion architecture in a regulated environment.
Xylity sourced from our data engineering partner network, specifically filtering for healthcare + Databricks experience. Within 5 days, we delivered 4 curated profiles — all with production lakehouse experience, 3 with healthcare data compliance background. The partner hired 3. The project kicked off 2 weeks ahead of the revised timeline.
Need Databricks talent?
Tell us the workload.
Share the Databricks workload, the cloud platform, and the timeline. We'll deliver curated profiles from engineers who've built lakehouse architectures in production.