Serve analytics, ML, and customers from the same data.
2 AI translations · Technology / SaaS
You build and maintain data pipelines (Airflow, dbt, Fivetran, Stitch, custom ETL/ELT) that move data from production systems into your warehouse (Snowflake, BigQuery, Redshift, Databricks) for analytics, ML training, and customer-facing features. Pipeline reliability is a perpetual challenge: schema changes in source systems break transforms, volume spikes cause failures, and data quality issues propagate downstream before anyone notices. You manage pipeline orchestration, monitoring (Monte Carlo, Great Expectations, Soda), and incident response when the dashboard says 'data last updated 47 hours ago.'
Data scientists build features independently, leading to duplicated effort and inconsistent feature definitions across models. Model deployment is manual and error-prone.