Data engineering for live, production workloads.
Data engineering built for production workloads, not just reporting. We design real-time data pipelines, streaming analytics, and warehouses that operations teams depend on minute by minute, and the BI and search layers that make the data usable.
Pipelines, warehouses, and the analytics on top
From ingestion to the dashboard an operator watches during a peak event.
Streaming architectures
Real-time pipelines on Kafka, OCI Streaming, and WebSockets. We have ingested live location data from over 23,000 vehicles concurrently, with streaming ETL feeding low-latency dashboards for fleet operations.
Spatial and real-time analytics
Live tracking and spatial joins tuned for accuracy, visualized with deck.gl and MapLibre over OpenStreetMap and Google Maps. Built for control rooms that need to know what is happening now, not five minutes ago.
Warehousing and data modeling
Warehouses and operational stores on PostgreSQL, Redshift, and Oracle, modeled around the questions the business asks. We have run spatial analytics on an Oracle data warehouse at fleet scale.
ETL and ELT pipelines
Batch and streaming pipelines that move, clean, and reconcile data across systems, including pipelines that feed ML systems and cross-reference user records against government occupational and employment datasets.
BI dashboards and analytics
Custom dashboards designed for the question, not the chart library. We also build BI dashboards generated directly from a plain-language question, so non-technical teams can get answers without waiting on an analyst.
Search and OCR
Search engines with crawling, indexing, and OCR over PDFs and scanned imagery, so documents become data you can query and analyze.
Built for the question, not the chart library
Most data projects fail quietly: the pipeline runs, the dashboard renders, and nobody trusts the numbers. We start from the decision the data has to support and work backward to the pipeline, the model, and the latency it needs.
- Start from the question. Your FDE works with the people who use the data before anything is modeled.
- Latency matched to the decision. Streaming where operators act in real time, batch where a scheduled load is enough.
- Accuracy you can check. Spatial joins, reconciliation, and transformations tuned and tested against real data.
- Traceable to the source. Where results face formal review, the audit trail cites the exact source data.
- Observable pipelines. Monitoring and alerting on data flows, so problems surface before the dashboard goes stale.
Data infrastructure runs on the cloud it is built for: see our cloud and DevOps engineering on AWS, Azure, GCP, and Oracle Cloud.
Data technologies we work with
Chosen for the workload, the latency it needs, and the platform you already run.
Streaming and ingestion
Moving data while it is still useful.
Storage and warehousing
Where the data lives and how it is modeled.
Analytics and search
Turning data into answers people can act on.
How you can engage
The same engineers, three ways to work with them. Pick the model that matches how defined the problem is and how much ownership you want to hand over.
Forward Deployed Engineering
An FDE works with your operations and data teams to define the questions, the sources, and the latency required, then leads a pod that builds and runs the pipelines.
How Forward Deployed Engineering works →Dedicated Engineering Pod
A senior-led pod owns a defined data platform, from ingestion and modeling through dashboards and deployment, and reports against milestones.
Build a dedicated engineering pod →Embedded Engineers
Senior data engineers join your team to add streaming, warehousing, or analytics depth. You direct the work day to day.
Add senior engineers to your team →Data systems we have shipped
Spatial analytics at scale
Kafka / WSA streaming architecture ingesting live location data from over 23,000 vehicles concurrently, with low-latency dashboards for fleet operations.
streamed live
Natural-language data workspace
RAG PipelineAn AI-powered workspace letting non-technical teams query enterprise data in plain language, with instant BI dashboards and document analysis.
enforced
Audit-grade compliance platform
Audit TrailA data platform cross-referencing user records against government datasets, with workflow and audit trails designed for formal review.
workflow
Common questions
Do you build streaming pipelines, batch pipelines, or both?
Both. We use streaming with Kafka, OCI Streaming, and WebSockets where the business needs to know what is happening now, and batch ETL or ELT where a scheduled load is simpler and cheaper. Most production systems end up with some of each.
Can you work with our existing database or warehouse?
Usually, yes. We work with PostgreSQL, Redshift, Oracle, MySQL, MS SQL, and MongoDB, and we start by understanding the data you already have before proposing anything new.
Can you make PDFs and scanned documents searchable?
Yes. We build search engines with crawling, indexing, and OCR over PDFs and scanned imagery, and document analysis that extracts insight from reports and contracts.
How does your data engineering connect to AI work?
Directly. Retrieval, natural-language querying, and model evaluation all depend on clean, current, well-modeled data. The same pod handles the pipelines and the AI and LLM engineering built on top of them.
Tell us what you’re trying to build.
A short conversation is enough to understand the problem, the systems involved, and the team required to move it forward.