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ETL Pipeline Development
We build data pipelines that extract from any source, transform with business logic, and load into your warehouse or lake. Orchestrated with Airflow, tested, monitored, and built to handle growth.
Data Engineering
Modern data stack for reliable, scalable pipelines
Production-ready data pipelines with documentation, monitoring, and knowledge transfer.
Well-tested Airflow DAGs, Spark jobs, or dbt models in version control with CI/CD.
Managed Airflow or Dagster deployment with schedules, alerting, and retry logic.
Automated checks for schema changes, null rates, freshness, and business rule validation.
Technical documentation, data dictionaries, and operational runbooks for your team.
A systematic approach to building data pipelines that last.
Map your data sources, understand schemas, identify transformation needs, and define target models.
Design pipeline DAGs, choose tools, plan for scale, and establish testing and monitoring strategies.
Implement pipelines with comprehensive testing, data quality checks, and documentation.
Production deployment with alerting, SLA tracking, and runbooks for your team.
Flexible options from single pipelines to full data platform builds.
Single data pipeline from source to warehouse with testing and monitoring.
$15,000 - $25,000
Multiple pipelines, orchestration platform, data quality framework, and team training.
$40,000 - $60,000
Ongoing pipeline development, maintenance, and optimization for your data platform.
$8,000 - $18,000/mo
Results from data pipeline implementations we've delivered.
"Reports that used to be 3 days stale now update hourly. The business finally trusts the numbers."
"We went from 47 manual data jobs to 12 automated pipelines. Freed up 2 FTEs for actual analysis."
"The data quality checks catch issues before they hit dashboards. No more embarrassing board meeting corrections."
"Reports that used to be 3 days stale now update hourly. The business finally trusts the numbers."
Modern cloud warehouses favor ELT (Extract-Load-Transform) since they can handle transformation at scale. We recommend ELT for most use cases, with ETL reserved for sensitive data that needs transformation before loading.
For simple, low-frequency pipelines, tools like Fivetran or AWS Glue may suffice. Airflow adds value when you have complex dependencies, custom logic, or need fine-grained control. We assess during discovery.
Pipelines include automatic retries, alerting, and idempotent design so reruns don't create duplicates. We also build backfill capabilities for historical data reprocessing.
Yes. We integrate with existing warehouses, BI tools, and orchestrators. If you're already using Snowflake, dbt, or Looker, we build pipelines that feed those systems.
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Share your data sources and goals. We'll assess architecture, tooling, and timeline in a 30-minute call.