I will build etl pipelines, pyspark workflows, and looker dashboard using airflow, sql

I build on AWS (Glue, Lambda, S3, Redshift, EMR), Azure (Data Factory, Databricks, Synapse Analytics), and GCP (Cloud Composer, BigQuery, Dataflow). I'll recommend the best platform based on your existing infrastructure, team skills, and budget — and I provide this recommendation for free before you

Apache Airflow is a workflow orchestration tool that schedules, monitors, and retries your data pipelines automatically. If you need data processed on a regular schedule (hourly, daily, event-driven), Airflow is essential. I design production-ready Airflow DAGs with proper retry logic, alerting, and

ETL (Extract-Transform-Load) processes and cleans data before loading it into your data warehouse — better for legacy systems and strict data governance. ELT (Extract-Load-Transform) loads raw data first and transforms inside the warehouse using tools like dbt — better for modern cloud stacks like S

Yes. I specialize in migrating from SQL Server, Oracle, MySQL, and flat files to cloud warehouses like Snowflake, BigQuery, and Redshift. This includes schema design, data type mapping, validation testing, incremental load setup, and historical data backfill — fully documented.

Every delivery includes full technical documentation: pipeline architecture diagram, DAG descriptions, transformation logic explanation, data dictionary, setup guide, and runbook for your team. My goal is that any data engineer on your team can understand and extend the pipelines independently.