Hire Databricks Developers
The top 3% of Databricks developers are already in our network. The 90-day replacement guarantee means you hire with confidence and build without interruption.
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Tecla: The AI talent partner for Engineering teams
Tecla's vetting covers four areas: AI-readiness, technical depth, soft skills, and English fluency. AI-readiness goes beyond tools and frameworks. It is how a candidate thinks about AI across the stack, from architecture to problem-solving to the way they approach their daily work. We assess the full picture before you see a single profile.
AI-Readiness
Thinking about and applying AI across tooling, architecture, and problem-solving.
Technical Depth
Hands-on assessment by our technical team, not an automated screening tool.
Soft Skills
Real communication and collaboration evaluated in context, not a personality quiz.
English Fluency
Assessed through actual technical discussion, not a written test.
Tecla is not a generalist staffing agency that added an AI filter. We are an AI-specialist talent network.
What our Databricks Engineers build for you
Data Pipeline Development & ETL
Production pipelines that ingest, transform, and deliver clean data at scale. Apache Spark, Delta Lake, PySpark, and SQL handling batch and streaming workloads without the technical debt.
Lakehouse Architecture & Migration
Modern lakehouse platforms designed with Delta Lake and Unity Catalog governance. Legacy migrations from Hadoop, Snowflake, or traditional warehouses handled without breaking your analytics team's workflows.
ML Engineering & MLOps
Data pipelines connected to ML workflows with proper versioning, governance, and automated retraining. MLflow for experiment tracking, Feature Store for feature engineering at scale.
Performance Optimization & Cost Management
Cluster performance monitored, Spark jobs optimized, Delta Lake tuned, and cost controls implemented before your cloud bill becomes a problem. Architectural reviews that catch bottlenecks early.
Senior Databricks Developers ready to join your team
These are representative profiles from our active network. Request your shortlist and we will match you with engineers fit for your stack, cloud platform, and data scale.
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Builds end-to-end ML and analytics pipelines on Databricks for e-commerce and retail tech companies. Specializes in Feature Store, AutoML, and integrating Databricks with modern data stacks. Known for reducing time-to-insight for data science teams through self-service platform tooling.
Leads enterprise Databricks implementations for Fortune 100 clients across financial services and healthcare. Deep expertise in Delta Live Tables, Unity Catalog governance, and building scalable lakehouse architectures. Has managed multi-team data platform migrations from legacy Hadoop and Redshift environments.
Designed multi-cloud data platforms for enterprise clients. Specializes in data governance and security frameworks. Led migrations from Snowflake and Redshift to Databricks.
Deployed production ML pipelines with automated retraining and monitoring. Expert in feature engineering at scale. Reduced model deployment time from weeks to days.
Transformed raw data into production-ready analytics for executive dashboards. Builds self-service BI infrastructure using medallion patterns. Strong collaboration with data science teams.
Architected distributed systems handling petabyte-scale datasets. Deep expertise in performance optimization. Cut processing costs by 40% through pipeline redesign.
Designed data platforms serving 200+ analysts. Expert in medallion architecture and Unity Catalog governance. Migrated legacy systems to modern lakehouse stacks.
Built real-time pipelines processing 5M+ daily events for fintech platforms. Specializes in lakehouse architectures and MLOps integration. Previously led data engineering at a Series B startup.
Why hire Databricks Developers through Tecla?
5-Day average match
We match you with qualified Databricks developers in 5 days on average. Traditional recruiting firms take 42+ days and that is before the notice period.
Same-day responsiveness
Full overlap with US business hours. When a pipeline fails or a cluster starts burning money, you get a response before the day ends, not the next morning.
The talent is there. You decide where they are based
Go US-based when the role needs it. Go nearshore when you want to ship more with the same budget. Tecla places senior Databricks developers in both markets, at the same standard.
Stop rehiring the same data engineering role every 18 months
Databricks knowledge compounds. A developer who understands your lakehouse architecture, Delta tables, and cluster configs gets more valuable over time. Our 97% year-one retention means that investment stays on your team.
Zero timezone hassle
Full overlap with US business hours. No more waiting overnight for responses or fixing Spark jobs alone at midnight while your data team waits for their morning reports.
Teams building with AI trust Tecla to hire
Eleven years, 500+ companies, 50,000+ vetted professionals. What they say about working with us.
Tecla is organized and provides a strong partnership experience. From hiring multiple engineers within weeks to maintaining consistent communication and feedback, they've shown real professionalism. Their follow-up and collaboration made the entire staffing process efficient and enjoyable.
Kristen Marcoe
I’m very happy with Tecla. Their support has improved our QA process, reduced bug reports by half, and made our onboarding process twice as fast. The team is responsive, cost-effective, and delivers high-quality candidates on time. Tecla has truly become a trusted extension of our internal hiring team.
Meit Shah
It was a pleasure working with Tecla. Their team quickly understood our hiring needs and found candidates that matched our technical requirements perfectly. Communication was seamless, and they were always quick to respond and deliver results. Tecla’s attention to quality made the entire experience smooth and efficient.
Mayya Bozhilova
Tecla successfully found candidates for our team and handled the entire process from scheduling to interviews. They were timely, responsive, and always kept communication flowing through email and messaging apps. I was really impressed with Tecla’s follow-up and thoroughness throughout the process.
Jessica Warren
Tecla's business model and team set our company up with engineers that we have the real possibility of working with long-term and can grow with our business. Tecla came in highly recommended, and their pace from introduction to engagement to presenting candidates was very fast.
David Bradley
Internally, we're moving much faster than we were without the remote engineers Tecla recruited for us and we've been able to implement far more features. Once we brought on our first full-time designer in South America, it made the quality of our user interface, product, and marketing efforts increase substantially.
Drew Batshaw
When we started our recruiting initiatives for LATAM developers, it was crucial for us to rely on a company that could provide deep local expertise to help us identify the best software developers in Latin America. The teams at Tecla really go the extra mile to understand our needs, which is what has made our partnership so successful!
Douglas Santos
Hire Databricks Developers in 4 simple steps
Tell us what you need
Share your cloud platform, data scale, and current stack. No lengthy forms. No back-and-forth for days. One focused call and we handle the rest.
Receive your shortlist within 3 to 5 business days
Every profile includes verified production experience, not self-reported skills. You are reviewing engineers who have built real lakehouse infrastructure, not completed a Spark certification.
Interview your top choices
See how they think through lakehouse architecture problems and explain optimization decisions. You are evaluating fit, not teaching fundamentals. Candidates arrive briefed on your product context.
Start working together in week 2 to 3
We handle contracts, compliance, and paperwork across borders. You focus on onboarding them to your pipelines, Unity Catalog, and data team workflow.
90-day replacement guarantee. If the match is not right, we find you another at no extra cost.
What is a Databricks Developer
A Databricks developer is the engineer who makes your data platform actually scale. They build production pipelines with Apache Spark and Delta Lake, design medallion lakehouse architectures, optimize Spark jobs so your cloud bill does not spiral, and connect your data infrastructure to ML workflows. Not a BI analyst who learned PySpark. Not a general data engineer who set up a few notebooks. The person you hire when your data volume has outgrown what ad-hoc scripts and traditional warehouses can handle.
When you hire Databricks developers, you get measurable improvements fast. Most companies see compute costs drop 40-60% after proper pipeline optimization. Data quality issues disappear. Analysts get answers in minutes instead of hours.
Here's where the ROI becomes obvious. Migrating from Snowflake? A Databricks specialist handles that without breaking your analytics team's workflows. Data scientists complaining that feature engineering takes forever? The right developer sets up Feature Store and automated pipelines that actually work.
Your cloud bill keeps climbing and nobody knows why? They'll fix it in weeks, eliminating unnecessary data shuffles, right-sizing clusters, and setting up proper cost controls.
Your job description filters candidates. Make it specific enough to attract qualified developers and scare off resume keyword stuffers.
Job Title
"Senior Databricks Engineer" beats "Data Wizard" every time. Be searchable. Include seniority level since someone with 3 years Spark experience can't architect an enterprise lakehouse yet.
Company Overview
Give real context. Your stage (seed, Series B, public). Your product (fintech platform, e-commerce analytics). Team size (5-person data team vs. 50+ engineers). Tech stack (AWS-based, migrating from Snowflake, real-time streaming focus).
Candidates decide if they want your environment. Help them self-select by being honest about what you're building.
Role Description
Skip buzzwords. Describe actual work:
- "Build medallion pipelines processing 500GB daily from Kafka"
- "Migrate our Redshift warehouse without breaking analyst queries"
Technical Requirements
Separate must-haves from nice-to-haves. "3+ years with PySpark" means more than "big data experience." Your cloud platform matters, AWS, Azure, and GCP implementations all differ.
Be honest about what you actually need. Streaming pipelines? Unity Catalog? ML integration? Say so upfront.
Experience Level
"5+ years data engineering, 2+ years Databricks production systems" sets clear expectations. Many strong developers learned by building systems, not through CS degrees. Focus on what they've shipped.
Soft Skills & Culture Fit
How does your team work? Fully remote with async communication? Role requires explaining architecture to non-technical stakeholders? Team values documentation?
Skip "team player" and "excellent communication", everyone claims those. Be specific about your actual environment.
Application Process
"Send resume plus 3-4 sentences about your most complex Databricks project" filters better than generic applications. Set timeline expectations: "We review weekly and schedule calls within 3 days."
Strong candidates explain the _delta_log directory, ACID transactions, and optimistic concurrency control. They connect it to real scenarios,multiple pipelines updating the same table without conflicts.
Experienced developers start with Spark UI, stage timelines, shuffle sizes, task distribution. They mention data skew, unnecessary shuffles, small file problems, wrong partitioning. Watch for systematic thinking versus random guessing.
This reveals understanding of layered architecture. Bronze (raw ingestion), silver (cleaned data), gold (business-ready). They should discuss Unity Catalog organization and handling schema changes without breaking downstream users.
Practical candidates check cluster usage patterns, left running overnight, oversized configs, retry loops. They review Spark UI for expensive operations and implement cost controls. This shows operational thinking beyond making code work.
Strong answers avoid "add more memory." They investigate operations collecting data to the driver, broadcast joins that got too large, or insufficient partitioning. Understanding distributed computing fundamentals matters here.
Their definition of "complex" matters. Technical complexity? Business logic? Operational constraints? Strong candidates explain trade-offs and what they'd change knowing what they know now.
Experienced developers acknowledge both have strengths. Databricks excels at unstructured data and ML integration. Snowflake wins for SQL analytics with simple data models. This reveals trade-off thinking.
Good answers: create clear SQL views, provide example queries, set up Databricks SQL endpoints, write helpful table descriptions. They enable non-technical users instead of gatekeeping.
What do they value? Correctness? Performance? Maintainability? Cost? Good answers mention specific issues like missing error handling or inefficient joins. Listen for constructive approach.
Neither answer is wrong. But if you're migrating a legacy system and they only want greenfield work, that's a mismatch. Watch for self-awareness about preferences.
Strong candidates negotiate scope (MVP first, full solution later) and communicate trade-offs clearly (speed means debt). Avoid candidates who always cave or never compromise.
The real cost to hire Databricks Developers with Tecla
US Salary Ranges
LATAM Salary Ranges
Tecla has Databricks developers in the US and Latin America. The production standards are identical on both sides. Timezone overlap, English fluency, technical depth. You pick where they are based.
Frequently asked questions
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See who is available for your stack this week
No commitment. No lengthy intake forms. A 30-minute call, a shortlist in 5 days, and a 90-day guarantee if the fit is not right.