Technologies
Data foundations that analysts trust and AI teams can build on
We build modern data platforms on Snowflake, Databricks, and BigQuery — with dbt for transformation, Airflow or Dagster for orchestration, and the governance and observability your auditors and ML teams will both ask for.
The Stack
Modern data engineering — ingestion to activation
Ingestion & CDC
Fivetran, Airbyte, Debezium, Kafka — batch and change-data-capture from operational stores into the warehouse with schema evolution.
Warehouses & Lakehouses
Snowflake, BigQuery, Databricks (Delta), Redshift, Iceberg on S3. We pick by workload, cost profile, and existing stack — not by vendor allegiance.
Transformation with dbt
Version-controlled SQL transformations, tests, documentation, lineage. The 'analytics engineering' layer that makes data trustworthy.
Orchestration
Airflow, Dagster, or Prefect. Idempotent pipelines, SLAs, alerting, and asset-aware retries. No more 3am 'did the job run?' Slacks.
Streaming & Real-Time
Kafka, Flink, ksqlDB, Materialize. Sub-second analytics where it matters; nightly batch where it doesn't.
Semantic Layer & BI
Cube, dbt Semantic Layer, or LookML. One metric definition, every tool. Plus dashboards in Looker, Metabase, Superset, or Power BI.
How We Work
Audit-first, governance-baked-in, observability before the CFO calls
Audit the current state
Map sources, downstream consumers, broken pipelines, and the metrics people actually use. Most data projects fail because nobody mapped this first.
Architecture decision
Warehouse vs lakehouse, batch vs streaming, build vs buy ingestion. Decisions documented with cost models — not vendor brochures.
Build the foundation
Ingestion, raw layer, staging models, marts. dbt project structured for hundreds of models. Tests and lineage from day one.
Governance & access
Row/column-level security, PII tagging, masking policies, audit logs. Compliance with DPDP, HIPAA, SOC2 baked in — not bolted on.
Observability
Data quality tests, freshness SLAs, anomaly detection on volume and distribution. You learn about broken pipelines before the CFO does.
Activate
Reverse ETL (Hightouch, Census) to push enriched data back to Salesforce, HubSpot, ad platforms. Feature stores for the ML team.
Audit the current state
Map sources, downstream consumers, broken pipelines, and the metrics people actually use. Most data projects fail because nobody mapped this first.
Architecture decision
Warehouse vs lakehouse, batch vs streaming, build vs buy ingestion. Decisions documented with cost models — not vendor brochures.
Build the foundation
Ingestion, raw layer, staging models, marts. dbt project structured for hundreds of models. Tests and lineage from day one.
Governance & access
Row/column-level security, PII tagging, masking policies, audit logs. Compliance with DPDP, HIPAA, SOC2 baked in — not bolted on.
Observability
Data quality tests, freshness SLAs, anomaly detection on volume and distribution. You learn about broken pipelines before the CFO does.
Activate
Reverse ETL (Hightouch, Census) to push enriched data back to Salesforce, HubSpot, ad platforms. Feature stores for the ML team.
Use Cases
What teams actually do with the platforms we build
Unified Customer 360
Stitch product, billing, support, and marketing data into a single customer view — then push it back to GTM tools.
Product Analytics
Event pipelines (Snowplow, Segment) into the warehouse. Funnels, retention, and A/B analysis on first-party data you control.
Financial Reporting
Audit-ready financial models in dbt. Drift-free metrics across BI tools. Faster month-end close.
AI-Ready Data
Feature stores, vector embeddings, and training datasets engineered for repeatability. The foundation real ML teams ask for.
Where We Start
Engagement shapes we deliver most often
Modern Data Stack Implementation
Stand up the full stack — Snowflake/Databricks/BigQuery + Fivetran/Airbyte + dbt + Airflow/Dagster + Metabase/Looker. Production-ready in 6–10 weeks.
Lakehouse Migration
Move from legacy warehouses or Hadoop clusters to a Delta/Iceberg lakehouse. Cost reduction, separation of storage and compute, ACID guarantees.
dbt Project Bootstrap
Set up a dbt project structured for scale — staging/intermediate/marts, tests, docs, CI, and Slim CI. Train your team to own it.
Streaming Pipelines
Kafka + Flink/ksqlDB pipelines for fraud detection, real-time personalization, or operational analytics. Exactly-once semantics, ordered processing.
Reverse ETL & Activation
Push warehouse-derived audiences and traits back to Salesforce, HubSpot, Iterable, Braze, ad platforms. Close the loop from analytics to action.
Data Governance Hardening
PII classification, masking policies, lineage, access reviews, audit logs. SOC 2 / DPDP / HIPAA controls implemented and documented.
Common Questions
Snowflake, Databricks, or BigQuery?
Do we still need ETL tools, or is dbt enough?
What's the ROI on a data platform rebuild?
How long before the team is self-sufficient?
What about real-time?
Domains we've shipped in
Stuck on a data platform that's slow, brittle, or expensive?
We migrate, stand up, or repair modern data stacks. Then we hand it back to a team that owns it.
Related Solutions
AI & Machine Learning
LLM integration, RAG systems, evals, fine-tuning, and production ML.
AI Agents
Agentic workflows with tool use, MCP, planning, and human-in-the-loop.
Enterprise Blockchain
Supply-chain provenance, tokenized assets, settlement rails — audited and production-grade.