70%
Databricks Lakehouse Migration → 70% lower warehouse compute cost
Trusted by startups and global leaders
Data Engineering Services We Deliver
HL7 and FHIR pipelines to a governed lakehouse.
Healthcare Data Pipeline Engineering
We build ingestion pipelines for HL7, FHIR, X12, and device data that run unattended.
Databricks ETL & Lakehouse Development
We build medallion lakehouses on Databricks and Delta Lake with Unity Catalog governance.
Data Warehouse Modernization
We migrate legacy warehouses and stored-procedure ETL to Snowflake, BigQuery, or Databricks.
Real-Time & Streaming Data Pipelines
We stream ADT events, vitals, and claims with Kafka and Structured Streaming.
Data Governance, Quality & PHI Controls
We add lineage, quality tests, de-identification, and HIPAA-grade access control.
AI-Ready Data Foundations
We build feature stores and vector indexes so AI agents work on trusted, current data.
Real Data Engineering Outcomes
Real numbers from pipelines we shipped — measured in production, not a pitch deck.
15m
Streaming HL7 & ADT Pipelines → nightly batch cut to 15-minute freshness
99.8%
Pipeline Quality Monitoring → 99.8% pipeline run reliability
80%
Automated Ingestion Framework → 80% less manual data prep
4x
Unity Catalog Governance Layer → 4x faster onboarding of new sources
120+
Production Pipelines Shipped → running across healthcare clients
Data Engineering Technologies We Work With
We pick tools for what your pipeline actually needs — with production experience across ingestion, storage, transformation, and governance.
Databricks & Lakehouse
We build medallion architectures on Databricks, Delta Lake, Unity Catalog, and Photon for ETL that scales without rewrites.
Cloud Data Warehouses
We model and load Snowflake, BigQuery, Redshift, and Azure Synapse — including migrations off legacy SQL Server and Oracle.
Orchestration & Transformation
We run dbt, Apache Airflow, Prefect, and Dagster so transformations are versioned, tested, and observable.
Streaming & Event Pipelines
We stream ADT, vitals, and claims events through Kafka, Kinesis, Flink, and Spark Structured Streaming.
Healthcare Data Standards
We parse and normalize HL7 v2, FHIR R4, CDA, DICOM, and X12 837/835 into analytics-ready models.
Governance, Lineage & Security
We add Great Expectations tests, OpenLineage tracking, PHI de-identification, and role-based access on every dataset.
Who Our Data Engineering Serves
Built for the teams that depend on data arriving clean and on time.
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Health Systems & Hospital IT
Health Systems & Hospital IT
One pipeline across EHR, lab, and billing systems.
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Payers & TPAs
Payers & TPAs
Claims, eligibility, and 837/835 feeds at scale.
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Life Sciences & Clinical Research
Life Sciences & Clinical Research
Validated pipelines for trial and lab datasets.
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AI & Data Science Teams
AI & Data Science Teams
Feature stores and vector indexes agents can trust.
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Platform & DevOps Teams
Platform & DevOps Teams
Orchestrated, monitored pipelines with clear ownership.
Data Engineering Built Around Real Workloads
Every pipeline we build serves a specific decision, report, or agent — not a generic data lake.
Clinical Data Warehouse
Encounters, orders, and results in one modeled layer.
HL7 & FHIR Ingestion
Interface feeds parsed, normalized, and reconciled.
Imaging & Device Data
DICOM metadata and device telemetry made queryable.
Lakehouse Migration
Legacy ETL rebuilt on Databricks and Delta Lake.
Claims & Revenue Pipelines
837, 835, and remittance data joined end to end.
Pipeline Observability
Freshness, volume, and schema checks with alerting.
Streaming Event Feeds
ADT and vitals events delivered in near real time.
PHI De-Identification
Safe-harbor and expert-determination datasets on demand.
AI Agent Data Layer
Feature stores and retrieval indexes agents query live.
Industries We Serve
Different industries have unique challenges and requirements. We build solutions that address the specific needs and workflows of your business sector.
Healthcare
Restaurant
eCommerce
Logistics
Social Networking
Games and Sports
Travel
Aviation
Real Estate
Education
On-Demand
Entertainment
Stop Rebuilding the Same Broken Pipeline
Most data problems are not analytics problems. They start upstream, in brittle interfaces, undocumented transformations, and batch jobs nobody owns. Our data engineering team rebuilds that foundation — from source audit to a governed lakehouse your teams and agents run on every day.
Our Process
How We Approach Data Engineering Engagements
Every pipeline project starts with the source systems you already have.
Source & Interface Audit
Every feed, format, and gap.
Target Architecture Design
Lakehouse layers and trade-offs.
Pipeline Build & Testing
Ingestion in slices, tested.
Governance & Validation
Lineage, quality, PHI access.
Cutover, Monitoring & Handover
Parallel loads, then alerting.
Data Engineering Tech Stack We Work With
We pick the right tool for each layer of your data platform, with honest guidance on what fits.
Databricks
Lakehouse & Warehouse
Delta Lake
Lakehouse & Warehouse
Snowflake
Lakehouse & Warehouse
Google BigQuery
Lakehouse & Warehouse
AWS Redshift
Lakehouse & Warehouse
Azure Synapse
Lakehouse & Warehouse
PostgreSQL
Lakehouse & Warehouse
ClickHouse
Lakehouse & Warehouse
dbt
ETL & Orchestration
Apache Airflow
ETL & Orchestration
Prefect
ETL & Orchestration
Apache Spark
ETL & Orchestration
AWS Glue
ETL & Orchestration
Apache Kafka
Streaming & Events
Apache Flink
Streaming & Events
Grafana
Streaming & Events
Kibana
Streaming & Events
Python
Languages & Libraries
SQL
Languages & Libraries
JavaScript
Languages & Libraries
Microsoft Azure
Cloud & Platform
Kubernetes
Cloud & Platform
Docker
Cloud & Platform
Terraform
Cloud & Platform
GitHub Actions
Cloud & Platform
MLflow
Cloud & Platform
Our Process
How We Deliver Data Engineering Projects
A decade of pipeline delivery shaped a process that catches bad data early instead of in the dashboard.
We profile every source — EHR, lab, claims, device — and document the real data quality.
We design the lakehouse layers, data models, and refresh cadence around actual use cases.
We build ingestion, transformation, and orchestration in iterations, validating each layer.
We add automated tests and reconcile against source systems before anything goes live.
We ship with CI/CD, freshness and volume alerting, lineage, and cost monitoring in place.
After launch we tune compute cost, add sources, and evolve models as the business changes.
01
Source Discovery & Profiling
We profile every source — EHR, lab, claims, device — and document the real data quality.
02
Architecture & Modeling
We design the lakehouse layers, data models, and refresh cadence around actual use cases.
03
Pipeline Development
We build ingestion, transformation, and orchestration in iterations, validating each layer.
04
Quality Gates & Reconciliation
We add automated tests and reconcile against source systems before anything goes live.
05
Deployment & Observability
We ship with CI/CD, freshness and volume alerting, lineage, and cost monitoring in place.
06
Support & Optimization
After launch we tune compute cost, add sources, and evolve models as the business changes.
Recognition & Partnerships
Recognized by leading industry partners.
Clutch 100 Fastest Growing AI Company
2025
Clutch Verified Partner
2024
Clutch Global Spring 2025
2025
AppFutura Top Developer
2024
ASSOCHAM Startup Member
2025
AWS Partner
2020
GoodFirms Top AI Copilot Developer
2023
Google Cloud AI Partner
2022
Sortlist Top AI Agency
2024
Trustpilot AI Services Excellence
2021
Good Analytics Starts With Boring, Reliable Pipelines
Dashboards and AI agents are only as good as the data underneath them. We build the ingestion, transformation, and governance layers that make everything above them trustworthy.
Start the Conversation
Frequently Asked Questions
[ 1 ]
What do data engineering services include?
Ingestion, transformation, orchestration, storage design, quality testing, and governance.
[ 2 ]
Do you build ETL pipelines on Databricks?
Yes — medallion lakehouses on Databricks and Delta Lake, governed with Unity Catalog.
[ 3 ]
Can you handle HL7, FHIR, and X12 claims data?
Yes — we parse and normalize HL7 v2, FHIR R4, CDA, DICOM metadata, and X12 837/835.
[ 4 ]
How long does a data pipeline project take?
A first production pipeline ships in 6–10 weeks; full platform builds run 4–6 months.
[ 5 ]
Do you migrate our existing warehouse or work with it?
Both — we modernize legacy ETL, or extend Snowflake, BigQuery, Redshift, and Synapse in place.
[ 6 ]
How do you keep PHI safe in the pipeline?
Encryption, role-based access, audit logging, and de-identified datasets for analytics.
[ 7 ]
Can these pipelines feed our AI agents and models?
Yes — we build feature stores and retrieval indexes agents and models query directly.
[ 8 ]
What support do you provide after go-live?
Runbooks, monitoring, cost tuning, schema-change handling, and new source onboarding.
Global presence
