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Built orchestration systems managing 500+ DAGs for fintech data platforms. Specializes in complex dependencies and error handling. Reduced pipeline failures from 15% to under 2% through proper DAG design.
Designed data orchestration infrastructure for e-commerce companies processing 10M+ events daily. Expert in Airflow at scale and infrastructure as code. Migrated legacy cron jobs to Airflow without downtime.
Architected containerized Airflow deployments on EKS handling 1000+ concurrent tasks. Deep expertise in monitoring, scaling, and cost optimization. Cut infrastructure costs by 45% through resource tuning.
Built ELT pipelines orchestrating dbt models and data transformations. Specializes in data quality checks and incremental processing. Strong collaboration with analytics teams on scheduling and dependencies.
Designed orchestration for ML pipelines and data warehousing workflows. Expert in custom operators and plugin development. Improved pipeline observability through custom monitoring and alerting.
Led Airflow implementations for enterprise clients across healthcare and finance. Specializes in complex workflow design and governance. Migrated 200+ legacy workflows to Airflow maintaining business continuity.
Most recruiting firms take 6+ weeks to find Airflow talent. We match you with qualified engineers in 5 days because we maintain a pre-vetted pool of 50,000+ developers.
Stop waiting overnight for pipeline fixes. Your Airflow developers work 0-3 hours different from US time, joining standups and debugging failures during your workday.
Senior Airflow engineers in Latin America cost $70K-$115K annually versus $180K-$250K+ in US tech hubs. Same expertise in DAG design, Kubernetes deployment, and production orchestration.
We accept 3 out of every 100 applicants. You interview engineers who've managed production Airflow deployments with hundreds of DAGs, not people who installed Airflow locally last week.
Our placements don't bounce after six months. Nearly all clients keep their Airflow developers past year one, proving we match technical skills and culture properly.
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"We couldn't find Airflow expertise locally at any price. Tecla connected us with an engineer who had managed similar scale deployments. He redesigned our DAG structure and cut our failure rate by 80% in the first two months."
Key result
Hired in 5 days, reduced pipeline failures from 12% to under 2%
"Traditional recruiting sent us backend engineers who claimed they knew Airflow. Tecla's vetting was different. The developer we hired understood DAG dependencies, backfilling strategies, and production monitoring, not just running hello world DAGs."
Key result
Reduced hiring time from 14 weeks to 6 days
"Our Airflow deployment was a mess, unstable, slow, constantly failing. The engineer from Tecla containerized everything, implemented proper resource limits, and set up monitoring. Our data team actually trusts the pipelines now.
Key result
Improved pipeline reliability from 85% to 99.2% uptime
Our Airflow developers build production DAGs that orchestrate complex data workflows. They work with Python operators, custom sensors, branching logic, and proper dependency management. Expect DAGs that handle failures gracefully instead of silently breaking your data.
Expert-level experience deploying Airflow on Kubernetes, Docker, or managed services like MWAA and Cloud Composer. They configure executors (Celery, Kubernetes), set up proper scaling, implement CI/CD for DAG deployment, and optimize resource allocation.
Deep expertise setting up observability for Airflow deployments. They implement custom alerting, log aggregation, performance monitoring, and SLA tracking. When pipelines fail at 3am, they've built systems that alert the right people with actionable context.
Our Airflow developers migrate legacy cron jobs, Luigi workflows, or custom schedulers to Airflow without breaking existing processes. They optimize slow DAGs, refactor complex dependencies, and implement incremental processing patterns that save compute costs.
Share your orchestration challenges and infrastructure setup. A quick call helps us understand whether you need someone to build new pipelines, optimize existing DAGs, or migrate from legacy systems.
Within 3-5 days, you'll see profiles matched to your tech stack. Every candidate has passed technical assessments, we've verified they've managed production Airflow deployments, not just completed tutorials.
Talk to candidates who fit your requirements. See how they approach DAG design, debug dependency issues, and think about scaling orchestration infrastructure.
Pick your Airflow developer and start building reliable pipelines. We handle contracts and logistics so you can focus on getting them access to your infrastructure and aligned with your data workflows.
An Apache Airflow developer builds and maintains data pipeline orchestration using Airflow's workflow management platform. Think of them as data engineers who specialize in making sure data jobs run reliably, in the right order, at the right time, not just writing the jobs themselves. The difference from general data engineers? Airflow developers have deep knowledge of DAG design patterns, dependency management, backfilling strategies, and production deployment considerations. They understand what makes orchestration different from just running scripts. These folks sit at the intersection of data engineering, DevOps, and software engineering. They're not just scheduling cron jobs, they're architecting systems that handle complex dependencies, retry failed tasks intelligently, and scale as workflow complexity grows. Companies hire Airflow developers when they're drowning in cron jobs that break mysteriously, scaling data pipelines beyond simple scripts, or migrating from legacy orchestration tools. The role grew as data teams realized reliable orchestration matters as much as the data transformations themselves.
When you hire Airflow developers, your data pipelines become predictable instead of surprising. Most companies see pipeline reliability improve from 80-85% to 98%+, debugging time drop by 60-70%, and data team productivity increase as they stop firefighting broken workflows.
Here's where the ROI shows up. Cron jobs failing silently and nobody notices for days? Airflow's monitoring and alerting catch failures immediately with context about what broke and why. Dependencies between jobs managed through tribal knowledge? Explicit DAG dependencies make workflows self-documenting.
Your data team spends half their time debugging why yesterday's pipeline didn't run? Good Airflow developers build retry logic, proper error handling, and observability that surfaces issues before downstream teams complain. Manual backfills taking days of engineering time? Airflow handles backfilling automatically with proper date logic.
Infrastructure costs climbing as workflows multiply? Airflow developers implement resource pools, task concurrency limits, and smart scheduling that prevents resource contention. Your pipelines scale without linearly scaling infrastructure costs.
Your job description filters candidates. Make it specific enough to attract qualified Airflow developers and scare off backend engineers who installed Airflow once.
"Senior Airflow Engineer" or "Data Engineer - Airflow" beats "Pipeline Wizard." Be searchable. Include seniority level since someone who's written a few DAGs can't architect production orchestration infrastructure yet.
Give real context. Your stage (seed, Series B, public). Your data stack (cloud platform, data warehouse, processing frameworks). Scale (dozens of DAGs vs. hundreds, batch vs. real-time). Team size (solo data engineer vs. 20-person data team).
Candidates decide if they want your environment. Help them self-select by being honest about what you're building.
Skip buzzwords. Describe actual work:
Separate must-haves from nice-to-haves. "3+ years managing production Airflow deployments" means more than "data pipeline experience." Your infrastructure matters, Kubernetes, Docker, AWS/GCP/Azure, managed Airflow services.
Be honest about what you need. DAG development? Infrastructure deployment? Migration from other tools? Monitoring and observability? Say so upfront.
"5+ years data engineering, 2+ years specifically with Airflow in production" sets clear expectations. Many strong developers came from Luigi, Oozie, or custom scheduler backgrounds. Focus on orchestration experience.
How does your team work? Fully remote with async? Role requires coordinating with multiple data teams? Team values documentation and runbook creation?
Skip "problem solver" and "self-starter", everyone claims those. Be specific about your actual environment.
"Send resume plus brief description of an Airflow deployment you managed and what scale/challenges you handled" filters better than generic applications. Set timeline expectations: "We review weekly and schedule calls within 3 days."
Good interview questions reveal production experience versus tutorial knowledge.
Strong candidates explain the scheduler parsing DAGs, creating task instances, the executor running tasks, and how state propagates. They discuss DAG serialization, scheduler heartbeat, and database interactions. Listen for understanding of Airflow internals, not just using it.
Experienced developers discuss task dependencies using bit shift operators or set_upstream/downstream, branching patterns, trigger rules for task D (all_success vs all_done), and how to visualize complex graphs. Watch for clarity in dependency management.
This reveals infrastructure knowledge. They should discuss executor choice (KubernetesExecutor vs CeleryExecutor), persistent volumes for logs, database configuration, autoscaling workers, and networking for worker pods. Listen for production deployment experience.
Practical candidates check for resource constraints, race conditions in dependencies, external service availability, task concurrency limits, and differences in data volume. This shows systematic debugging versus guessing.
Strong answers investigate task duration and resource usage, implement pools to limit concurrency, right-size executor resources, use sensors efficiently instead of polling, and consider smarter scheduling to spread load. Avoid candidates who only suggest "add more workers."
Their definition of success matters. Reliability? Scalability? Developer experience? Strong candidates explain architectural decisions, how they handled growth, and what they learned from incidents. Vague answers about "running pipelines" signal thin experience.
Experienced developers acknowledge Airflow adds complexity. They discuss when it's worth it (complex dependencies, need for monitoring, backfilling requirements) versus when cron suffices (simple independent jobs). This reveals judgment about tool selection.
Good answers: create reusable DAG templates, build simple interfaces or forms for common patterns, provide clear documentation and examples, and establish guardrails for common mistakes. They enable self-service without chaos.
What do they focus on? Resource pools? Scheduling coordination? Communication? Good answers mention technical solutions (pools, priority weights) and team coordination. Listen for collaborative problem-solving.
Neither answer is wrong. But if you're stabilizing a messy deployment and they only want greenfield work, that's a mismatch. Watch for self-awareness about preferences.
Strong candidates discuss starting with working pipelines, adding complexity as needs emerge, and when technical debt becomes worth addressing. Avoid candidates who over-engineer upfront or never refactor.
Location dramatically changes your budget without changing technical capability.
A team of 5 mid-level Airflow developers costs $650K-$900K annually in the US versus $300K-$425K from LATAM. That's $350K-$475K saved annually while getting identical expertise in DAG design, Kubernetes deployment, and production orchestration.These developers from LATAM join your on-call rotation, fix pipeline failures in real-time, and work your hours. The savings reflect regional cost differences, not compromised expertise.
Connect with Developers from Latin America in 5 days. Same expertise, full timezone overlap, 50-60% savings.
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