Hire dbt Developers
Connect with elite nearshore dbt developers from Latin America in 5 days, at a fraction of US costs. Build your analytics engineering team while saving up to 60%, without compromising on quality or timezone compatibility.
dbt Developers Ready to Start
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Develops dbt models for operational reporting and business intelligence workflows. Experience instrumenting dbt projects with tests and documentation from day one. Working on advanced dbt features including metrics layers, exposures, and cross-project references.
Analytics engineer building dbt transformation layers for SaaS product and revenue analytics. Experienced with incremental model strategies, source freshness monitoring, and structuring staging, intermediate, and mart layers for growing data teams. Works on builds and legacy SQL migrations.
Builds dbt projects on Databricks for large-scale analytical workloads, combining SQL models with Python transformations. Specializes in data quality testing, model documentation, and structuring dbt projects for multi-team collaboration. Experience in retail and logistics analytics.
Builds analytics engineering stacks with dbt at the transformation layer. Deep experience integrating dbt with ingestion tools and orchestration platforms. Has led migration projects moving teams from ad-hoc SQL to governed, tested dbt model environments.
Builds dbt transformation pipelines for marketing, product, and finance analytics teams. Specializes in dimensional modeling, slowly changing dimensions, and building semantic layers that downstream BI tools consume reliably. Background in enabling self-service analytics for non-technical stakeholders.
Architects dbt-based transformation layers for enterprise data warehouses, managing hundreds of models across multiple data domains. Deep experience with dbt Core and dbt Cloud, modular project structure, and CI/CD for analytics pipelines. Has built analytics engineering infrastructure for fintech and e-commerce companies processing billions of rows monthly.
What Makes Tecla Different For Hiring dbt Developers
3% Acceptance Rate
Out of every 100 dbt developers who apply, 3 pass our technical evaluations. The candidates you interview have built modular dbt projects, written meaningful tests, and shipped analytics infrastructure that data teams actually depend on.
5-Day Candidate Match
dbt developers who've built production analytics engineering stacks are in demand. We maintain a vetted pool so you're reviewing qualified profiles within 5 days of defining your requirements, not 6 weeks later.
40–60% Salary Savings
Hiring nearshore dbt developers in Latin America costs significantly less than US-based equivalents. The analytics engineering depth is comparable. The economics aren't.
97% Year-One Retention
dbt projects accumulate complexity over time. A developer who understands your model architecture, source conventions, and business logic becomes harder to replace the longer they stay. Our retention rate means that investment compounds rather than resets.
0–3 Hour Timezone Overlap
Analytics engineering work involves close collaboration with data analysts, BI developers, and stakeholders across the business. Latin American developers work your US hours, keeping those conversations synchronous.
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Real Results From Real Clients
"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."
"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."
"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."
The Standards Every Tecla dbt Developer Meets
Building modular, well-documented dbt projects with clear staging, intermediate, and mart layer separation. Our dbt developers work with dbt Core and dbt Cloud on Snowflake, BigQuery, Redshift, and Databricks to deliver transformation layers that data analysts and BI teams can trust and build on.
Expert-level experience implementing dbt tests, source freshness monitoring, custom generic tests, and dbt-expectations to catch data quality issues before they reach dashboards. They build testing frameworks that give data teams confidence in model output without requiring manual validation.
Deep expertise designing dbt project structures for multi-team environments: package management, cross-project references, incremental model strategies, and CI/CD pipelines that validate model changes before they hit production. Plus strong knowledge of dimensional modeling and semantic layer design.
Our dbt developers write model documentation that data analysts can actually use, build lineage graphs that make dependencies visible, and structure projects so new team members can contribute without a two-week onboarding. They treat documentation as part of the work, not an afterthought.
How Our Hiring Process Works
Share Your Requirements
Review Matched Profiles
Run Your Interviews
Make Your Hire
Our Hiring Models
Two structures for bringing nearshore dbt expertise onto your team.
True Cost to Hire dbt Developers: US vs. LATAM
Analytics engineering has become a distinct and valued discipline in data-mature organizations, and dbt developer compensation in the US reflects that. Total hiring investment depends heavily on where that developer is based.
US full-time hires carry overhead that adds up quickly. Benefits, payroll taxes, recruiting fees, and administrative costs typically add 35–45% to base salary before a single model gets written.
US Full-Time Hiring: Hidden Costs
Senior dbt developers in the US command $155K–$210K base. The fully-loaded cost is considerably higher once overhead is added.
- Health insurance: $12K–$18K
- Retirement contributions: $9.3K–$12.6K (~6% of base)
- Payroll taxes: $12.4K–$16.8K (~8% of base)
- PTO: $7.75K–$10.5K (~5% of base)
- Administrative costs: $6K–$9K
- Recruitment costs: $23.25K–$31.5K (~15% of base)
Total hidden costs: $70.7K–$98.4K per developer
Adding base compensation brings total annual investment to $225.7K–$308.4K per dbt developer.
LATAM Hiring Through Tecla (Per Developer, Annually)
All-inclusive rate: $88K–$120K
One rate covers developer compensation, regional benefits, payroll taxes, paid time off, HR administration, technical screening, and legal compliance. No recruiting markup. No hidden costs at renewal.
Tecla places software developers from Chile and the broader Latin American market in the US for years, including analytics engineers with deep dbt experience. Your dbt developer is in your data warehouse, building and testing models, while you focus on the analytical questions your business needs answered.
The Real Savings
A senior dbt developer in the US costs $225.7K–$308.4K annually once overhead is factored in. Tecla's all-inclusive rate: $88K–$120K. That's $105.7K–$188.4K saved per developer (47–61% reduction).
A team of 5 nearshore dbt developers: $1.13M–$1.54M annually in the US versus $440K–$600K through Tecla. Annual savings: $690K–$940K, with the same dbt modeling depth, English fluency, and timezone alignment.
Transparent all-inclusive pricing from day one. No recruiting fees or placement costs. Resources replaceable at no additional cost during the 90-day trial.
What Is a dbt Developer?
dbt developers, often called analytics engineers, build and maintain the transformation layer that turns raw data in a warehouse into clean, tested, and documented datasets that analysts and BI tools consume. They work at the intersection of data engineering and analytics, using dbt to bring software engineering practices to SQL-based data transformation.
dbt developers own the middle layer of the modern data stack. Ingestion tools bring data in. dbt transforms it into a form that's reliable, well-defined, and ready for analysis. BI tools and data scientists consume what dbt produces. Without this layer, most data teams end up with dozens of untested SQL scripts, inconsistent metric definitions, and analysts who don't trust their dashboards.
What separates a strong dbt developer from someone who's taken a dbt tutorial is their understanding of project design. How to structure models so a five-person data team can collaborate without stepping on each other. How to write tests that actually catch the failures that matter. How to build incrementally without creating the subtle bugs that appear months later.
Companies hire dbt developers when their data transformation work has grown past what a single analyst managing a folder of SQL files can handle. The warehouse is in place. The data is arriving. What's missing is someone who can impose order on the transformation layer and make it something the whole team can trust.
Business Impact
When you hire a dbt developer, the data that flows into your dashboards and models stops being a source of doubt and starts being a foundation people can build on.
Metric consistency: Centralized business logic in dbt models means "revenue" means the same thing in every dashboard, every report, and every analysis across the organization.
Data quality: Automated testing on every model run catches bad data before it reaches analysts, reducing the time spent investigating dashboard discrepancies.
Analyst productivity: When transformation logic is documented and reliable, analysts spend their time on analysis rather than on rebuilding the same SQL someone else already wrote.
Onboarding speed: A well-structured dbt project with clear lineage and documentation lets new data team members contribute in days rather than weeks.
A job description that asks for "SQL experience and knowledge of dbt" will attract analysts who've run a few dbt models locally. The right description filters for analytics engineers who've designed production dbt projects, managed model dependencies at scale, and built testing frameworks that data teams actually rely on.
What Role You're Actually Filling
State what the transformation layer currently looks like and where it needs to go. "Redesign our 200-model dbt project to support five additional data domains without increasing pipeline runtime" is a real challenge a qualified candidate can respond to. "Help us with our data" is not.
Be specific about your warehouse and orchestration environment. Snowflake with dbt Cloud is a different working context than Databricks with Airflow. If your data layer feeds into application endpoints or APIs, Node.js developers are often part of the same hiring cycle as analytics engineers.
Must-Haves vs Nice-to-Haves
List specific disqualifiers. "Designed and maintained a dbt project with 100+ models serving multiple downstream BI tools" is meaningful. "Familiarity with dbt" is not. Include the data warehouse platform, the BI tools downstream consumers use, and the orchestration setup if relevant.
Separate required from preferred. Python dbt models are increasingly common, but if someone has deep SQL dbt experience and can learn Python transformations, don't lose them to an overly strict requirements list. Focus on what truly disqualifies versus what's a nice addition.
Describe how this person will work with the rest of the data team. Are they the sole analytics engineer, working alongside data engineers, or managing a small team? If your stack extends into Salesforce reporting or revenue operations, Salesforce admins are a common pairing with dbt-focused analytics engineering work.
How to Apply
Ask candidates to describe the dbt project structure they're most proud of and why they made the organizational decisions they did. This surfaces people who think carefully about project design, not just people who can write a working model.
Give timeline expectations upfront. dbt developers with production experience are typically evaluating multiple opportunities. Knowing when they'll hear back respects their time and sets a professional tone from the first interaction.
Good dbt interview questions reveal how candidates think about project design and data quality, not just whether they know the syntax.
What it reveals: Real project design experience, not just model-writing ability. Listen for discussion of package structure, tagging strategies, how they'd handle shared staging models versus domain-specific marts, and how they'd manage deployment without one team blocking another. Someone who's designed for a multi-team environment thinks about this very differently from someone who's worked solo.
What it reveals: How seriously they treat data quality as part of their work. Look for discussion of built-in tests versus dbt-expectations, how they prioritize testing effort across models at different layers, and what criteria they use to define when a custom test is worth the investment. Strong candidates treat testing as part of engineering, not as optional documentation.
What it reveals: Honest self-assessment about what makes a dbt project durable. Listen for specific design decisions and what they protect against: orphaned models, undocumented business logic, untested source freshness, naming inconsistencies. Candidates who've maintained production dbt projects know where these failures live.
What it reveals: Experience with real production incidents in analytics engineering environments. Look for a clear incident description, how the issue propagated, and what process change (CI/CD, testing coverage, PR review) they added as a result. This story is common for anyone who's shipped production dbt work.
What it reveals: How they navigate the tension between speed and maintainability in a data team. Watch for candidates who understand why analysts make this choice and have practical approaches for making the dbt path faster, rather than just enforcing a policy. Strong candidates make the right path the easy path.
What it reveals: Collaboration style and how they manage dependencies across a data team. Strong candidates describe specific practices: deprecation warnings, communication protocols for breaking changes, how they use dbt's built-in documentation and lineage to make dependencies visible to everyone who needs to know.
What it reveals: What kind of work energizes them and where they're most effective. Infrastructure-oriented analytics engineers and domain-embedded ones have different strengths. The mismatch between this preference and the actual role structure is one of the more common causes of early attrition in analytics engineering hires.
Frequently Asked Questions
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Connect with Developers from Latin America in 5 days. Same expertise, full timezone overlap, 50-60% savings.
