About the service
I build data pipelines on Apache Airflow that do their work every day quietly and without manual supervision. I take on the entire ETL/ELT cycle: pulling data from APIs, files, queues and operational databases, bringing it to clean typed tables and loading it into your data warehouse on a schedule you can trust. Every process is described as a DAG with explicit dependencies, so tasks run in the right order and never start before their input data is ready.
Most of the work goes into reliability. I set up sensible retries with exponential backoff, idempotent tasks that can be restarted without duplicating rows, and alerts that reach you in Slack or email the moment something fails or a run takes too long. Between stages I add data quality checks to catch bad sources before they poison your reports, and I connect monitoring: execution time, backlog and success rate. Reloading history becomes a single command, not a weekend of manual scripts.
I integrate with what you already have — PostgreSQL, MySQL, ClickHouse, Snowflake, BigQuery, S3-compatible storage, dbt models and REST services — and I document every DAG so your team can develop it further. You get a version-controlled repository, clear deployment and orchestration that's transparent, not mysterious.
— DAG design, scheduling, dependencies, sensors and backfills
— Retries, idempotence, SLA, alerts and data quality control
— Integrations: data warehouses, databases, object storage, APIs and dbt
Most of the work goes into reliability. I set up sensible retries with exponential backoff, idempotent tasks that can be restarted without duplicating rows, and alerts that reach you in Slack or email the moment something fails or a run takes too long. Between stages I add data quality checks to catch bad sources before they poison your reports, and I connect monitoring: execution time, backlog and success rate. Reloading history becomes a single command, not a weekend of manual scripts.
I integrate with what you already have — PostgreSQL, MySQL, ClickHouse, Snowflake, BigQuery, S3-compatible storage, dbt models and REST services — and I document every DAG so your team can develop it further. You get a version-controlled repository, clear deployment and orchestration that's transparent, not mysterious.
— DAG design, scheduling, dependencies, sensors and backfills
— Retries, idempotence, SLA, alerts and data quality control
— Integrations: data warehouses, databases, object storage, APIs and dbt
