Data Engineering

Pipelines, data models, and event streams that get the right data to the people and systems that need it. Ingestion from internal and third-party sources, enrichment, and storage designed around the questions the business actually asks.

Data pipelinesData modelingEvent streamingWorkflow orchestrationThird-party enrichment

Industries

What it covers

Batch and streaming ingestion from internal systems and third-party sources

Data modeling in PostgreSQL and analytical stores

Durable workflow orchestration with Temporal

Event streaming with NATS JetStream and OpenTelemetry

Search over consolidated data with Typesense

Data quality checks and pipeline observability

Typical work

Enrichment pipelines that consolidate third-party data sources

Data consolidation across systems that don't talk to each other

High-volume ingestion for media, telemetry, and device data

Data platforms for regulated records, including PHI

How I think about it

Most analytics and automation projects stall on data, not tooling. The data is scattered, stale, or locked in a system nothing else can reach. I start there: get it accessible, modeled, and trustworthy, with a data path a security review can follow.

Other expertise

Agentic Engineering

Coding agents as part of the engineering system. Harnesses, guardrails, quality gates, and telemetry.

Automation & Applied AI

Manual work mapped and measured, then automated where it pays off. Models chosen on evals and cost, with guardrails.

Platform & Infrastructure

AWS and GCP platforms defined in code. Kubernetes, GitOps delivery, observability, and cost control.

Security & Compliance

Identity, least privilege, audit trails, and data boundaries designed in. Architecture that holds up under HIPAA, SOC 2, and ISO 27001 review.

Product Engineering

Web, mobile, and backend products from first version through launch and iteration. Full stack, from data model to interface.