We integrate SaaS apps, databases, flat files, SFTP, object storage, webhooks, and REST/GraphQL APIs. Pipelines normalize schemas, handle pagination, throttling, and watermarking, then land data reliably into staging layers with automatic schema evolution and metadata capture for downstream processing and reconciliation across environments.

Using dbt, SQL, and Talend components, we implement star schemas, slowly changing dimensions, audit columns, and privacy rules. Transformations are versioned, test-backed, and documented, producing reusable gold datasets for BI, ML, and operational exports with predictable performance and governance across teams and tools.

We design DAGs with dependencies, retries, SLAs, sensors, and event triggers. Task logs, metrics, and alerts provide deep visibility. We containerize workers, scale executors, and secure connections via secrets backends, delivering robust scheduling for batch, micro-batch, and hybrid streaming ingestion patterns across clouds and on-prem.

We enforce data contracts and run quality checks with Great Expectations or Soda. Rules validate schema, nulls, ranges, and referential integrity. Failures produce quarantined datasets, alerts, and automated issue tickets, preventing bad data from reaching warehouses, dashboards, or machine learning models used by business stakeholders.

We implement column-level lineage, business glossaries, and PII tagging using OpenLineage, Marquez, or cloud catalogs. Stakeholders see where data originates, how it transforms, and who consumes it, enabling audit readiness, impact analysis, and safer change management across interconnected pipelines and dependent analytics assets.

We reduce warehouse and compute spend via partitioning, clustering, incremental models, pushdown ELT, caching, and concurrency tuning. Pipelines scale elastically and pause when idle. Monitoring reveals hotspots, enabling targeted refactors that lower cost while maintaining freshness, reliability, and SLA compliance across critical business datasets and reports.