Hire senior Databricks engineers for your lakehouse
Senior Databricks engineers who build Spark pipelines, govern data with Unity Catalog and ship ML workloads on your lakehouse, embedded in your team on US hours.
By the Ryz Labs team · Updated October 2026
Hiring Databricks engineers through Ryz gets you senior Latin American engineers who can build, tune and govern a lakehouse that serves both analytics and machine learning. They are top 1% of the candidates we interview, they embed in your team and workspaces, and they work within an hour of US time zones.
What our Databricks engineers work on
Our Databricks engineers work across the platform: PySpark and Spark SQL, Delta Lake, Auto Loader, Lakeflow Declarative Pipelines (formerly Delta Live Tables), Jobs, Unity Catalog, MLflow and Databricks Asset Bundles for deployment. They run on AWS, Azure or GCP. Typical projects:
- Medallion architectures (bronze, silver, gold) with Auto Loader ingestion and clear data contracts between layers.
- Migrations from Hadoop, on-premises Spark or legacy ETL tools to Databricks, with parity checks on output.
- Unity Catalog rollouts: metastore design, catalogs and schemas per domain, grants, lineage and external locations.
- Spark job tuning and cluster policy work that cuts runtime and DBU spend.
- Structured Streaming pipelines from Kafka, Kinesis or Event Hubs into Delta tables.
- Feature pipelines, model training and tracking with MLflow, and model serving endpoints.
- Retrieval data for LLM applications using Delta tables and Databricks Vector Search.
Skills we vet for
- Spark internals. Shuffles, partitioning, broadcast joins, adaptive query execution, skew handling and reading the Spark UI.
- Delta Lake. ACID transactions, MERGE, schema evolution, OPTIMIZE, liquid clustering or Z-ordering, VACUUM and change data feed.
- Unity Catalog governance. Three-level namespace, privileges, row filters and column masks, lineage and audit logs.
- Pipeline design. Declarative pipelines with expectations, Jobs with task dependencies, retries and idempotent backfills.
- Streaming. Checkpointing, watermarks, trigger modes and recovering a stream after a bad deploy.
- Cost control. Cluster sizing, job versus all-purpose compute, serverless options, Photon and cluster policies.
- Engineering practices. Asset Bundles, Git folders, CI/CD, unit tests for PySpark and promotion across workspaces.
- ML workflow. MLflow tracking and model registry in Unity Catalog, feature tables and batch or real-time inference.
How we vet Databricks engineers
Our recruiters source engineers who have run Databricks workloads in production, including the ones that failed at 3 a.m. Our in-house ARC system ranks the pipeline. Candidates complete structured NTRVSTA AI interviews covering Spark tuning, Delta internals and governance. Recruiters review every candidate before and after, then send a curated shortlist. AI scores are advisory, and humans make the decisions.
Sample interview topics
- A join stage spends most of its time on a few tasks. How do you confirm skew in the Spark UI, and which fixes would you try in what order?
- Design a MERGE-based pipeline for late-arriving updates that stays correct when the job reruns.
- A Delta table has thousands of small files and queries are slow. What causes it, and how do you fix it without blocking writers?
- Plan a Unity Catalog migration for three workspaces that each have their own Hive metastore and overlapping table names.
- How would you set up CI/CD so a pipeline change is tested on sample data before it reaches production?
Ways to hire Databricks engineers
| Option | Best for | Trade-offs |
|---|
| Freelance marketplace | A single notebook or proof of concept | Notebooks built in isolation rarely become maintainable production pipelines. |
| Staffing or recruiting agency | Roles defined by a list of Spark and cloud keywords | Keywords do not show whether someone can tune a skewed job or design governance. |
| In-house recruiting | A permanent lakehouse platform team | Slow to staff, and hard to evaluate without an experienced Spark engineer interviewing. |
| Ryz Labs staff augmentation | Adding senior Databricks engineers to your data or ML team | You own the roadmap and standards. Best with an existing platform or a defined migration. |
| Ryz Labs AI pod team | Building an ML or LLM system on the lakehouse end to end | A dedicated pod with data, ML and backend engineers. Scoped as a team with a plan up front. |
If you want a short, self-serve freelance gig, or a strategy consultancy to design a multi-year data program without hands-on engineers, Ryz is not the right fit.
Why hire Databricks engineers from Latin America
Lakehouse work crosses team lines. Data scientists need a feature table, analysts need a gold table, and platform teams care about cluster policies. Engineers who share your working day can settle those questions in a quick call instead of a long async thread across time zones.
Senior Spark engineers in the region have often worked on large-scale data platforms for ecommerce, payments, telecom and media companies. They write clear design docs in English and are used to explaining cost and performance trade-offs to platform owners.
Related roles
FAQ
Do your engineers work on Databricks on Azure, AWS or GCP?
All three. Azure Databricks and Databricks on AWS are the most common. Cloud-specific details like networking, storage credentials and identity are part of what we vet.
Can they also handle ML and LLM work on Databricks?
Many can, using MLflow, model serving and Vector Search. For a full AI system with backend integration and evaluation, an AI pod team is usually the better fit.
What does it cost?
Custom quote, scoped per team. Before signing, you get a plan, a price and the names of the people who would do the work.
How does contracting work, and do they overlap with our hours?
You sign one contract with Ryz. Our engineers work with us as independent contractors, and we handle paying them. They work within an hour of US time zones.
Python or Scala?
Most current Databricks work is PySpark and SQL. If you have Scala jobs to maintain or migrate, tell us and we will match engineers with Scala Spark experience.
Questions we didn't answer? Email info@ryzlabs.com.