Data pipelines that
actually work in prod
From raw event streams to reliable analytics warehouses — we build the data infrastructure your business decisions depend on.
The full data engineering stack
Real-Time Data Pipelines
Stream processing architectures using Kafka and Apache Spark that handle millions of events per day with sub-second latency.
Data Warehouse Design
Scalable warehouse schemas on Snowflake, BigQuery, or Redshift — built for analytics workloads, not OLTP guesswork.
ETL / ELT Engineering
Robust data transformation pipelines with dbt, AWS Glue, and Airflow. Clean, tested, documented transformations your analysts can trust.
BI & Dashboard Delivery
End-to-end from raw data to insight: Metabase, Looker, Power BI, or custom React dashboards wired to your warehouse.
ML Feature Engineering
Feature stores and data prep pipelines that feed your models with clean, versioned, reproducible feature sets.
Data Governance & Quality
Data contracts, lineage tracking, and automated quality checks so bad data never reaches production dashboards.
Technologies We Work With
Real pipelines, real outcomes
Every engagement starts with understanding your data sources, business questions, and team constraints — then we build for your specific situation.
50M-Event Streaming Pipeline
An e-commerce platform was flying blind — pageviews, cart actions, and purchases landed in the warehouse 24 hours late. We rebuilt their ingestion layer using Kafka + Apache Spark Structured Streaming, cutting data latency from 24 hours to under 90 seconds.
Outcomes
- 50M+ events processed per day at peak
- Data latency reduced from 24 hours to <90 seconds
- Clickstream, cart, and revenue data unified in one schema
- Self-healing pipeline with dead-letter queue and alerting
Stack
Legacy SQL Server to BigQuery Migration
A manufacturing firm ran 40+ reports off a 10-year-old SQL Server. Queries took 20+ minutes and analysts had duplicated logic across hundreds of stored procedures. We migrated to BigQuery with a dbt model layer — consolidating logic, adding tests, and cutting query times dramatically.
Outcomes
- Full migration of 12TB of historical data to BigQuery
- Query runtimes cut from 20+ minutes to under 90 seconds
- dbt model layer with 100% test coverage on critical metrics
- Rollback capability maintained throughout migration
Stack
Multi-Source ETL & Reporting Hub
An insurance agency pulled data from five disconnected systems — a policy management platform, a CRM, a claims tool, a payment processor, and spreadsheets. Analysts spent 3 days per month stitching it together manually. We built a unified ETL pipeline and Metabase dashboard layer that produces the same reports overnight.
Outcomes
- 5 source systems consolidated into a single Redshift warehouse
- Monthly reporting time reduced from 3 days to under 2 hours
- Real-time KPI dashboards for agency principals
- Historical data preserved back to 2018
Stack
Fraud Detection Feature Store
A fintech startup had a promising fraud detection model but inconsistent training data — features computed differently in notebooks vs. production. We built a feature store with versioning, backfill capabilities, and automated freshness checks that the DS team could trust for both training and inference.
Outcomes
- Unified feature definitions across training and production
- Feature freshness monitoring with Slack alerting
- 3-year historical backfill for model retraining
- Model accuracy improved 14% after consistent feature delivery
Stack
Ready to build something
that actually works?
Book a 30-minute strategy call. No sales pitch — just an honest conversation about your project and the best way to approach it.
30-min call · No commitment · Response within 24 hours