Cloud cost optimization
Data platforms can quietly burn more than the applications they serve. We build FinOps discipline into architecture: tiered storage, incremental pipelines, warehouse auto-suspend, and honest separation of dev/test/prod—so engineering keeps shipping while CFOs see predictable curves on GCP, AWS, and Azure.
Representative capabilities—scoped to your cloud, warehouse, and compliance posture.
Methodology, ownership, and runbooks your procurement and platform teams can inspect—across GCP, AWS, Azure, Snowflake, Databricks, Airflow, and legacy sources such as Oracle.
Tagging strategies and logical warehouses per domain make costs attributable. We define unit metrics—cost per million rows landed, per model refresh, per dashboard session—so leaders can decide what to keep, cache, or retire.
Snowflake resource monitors, Databricks cluster policies, and BigQuery reservations are configured with guardrails—not surprise hard stops in production.
We pick the cheapest safe pattern per workload: burst-friendly serverless ingestion, steady ETL on reserved capacity, and interactive BI on separate endpoints to avoid cross-talk billing explosions.
Spot and preemptible patterns for fault-tolerant batch reduce CPU bills when SLAs allow.
Iceberg/Delta maintenance, compaction, and orphan file cleanup shrink lake storage. S3 Intelligent-Tiering, GCS Autoclass, and Azure cool/archive tiers are applied with retrieval SLAs documented.
Airflow DAG audits find overlapping extracts from Oracle and SaaS sources. We consolidate to single CDC streams where possible—less CPU, fewer failure modes, happier auditors.
Return to the data engineering hub for the full platform narrative, or open another enterprise focus area below.
Answers for data leaders, platform owners, and procurement—without hand-wavy claims.
Share your current warehouse, orchestration stack, and success metrics—we'll propose a phased path with clear validation gates.
