Healthcare Data Engineering Services for Genomics, Clinical Labs, and Life Sciences
Your AI initiative, audit prep, or new lab accreditation is only as strong as your data infrastructure.
Most healthcare data platforms were built before genomics went mainstream, before FHIR became mandatory, before AWS HealthOmics existed, and before HHS OCR expanded enforcement. NonStop builds healthcare data engineering that’s HIPAA-compliant by architecture, not by slide deck.
Check the boxes that apply to your organisation. If three or more apply, your data infrastructure is likely limiting your growth, compliance, and AI capabilities.
Scientists and data analysts spend significant time cleaning and wrangling data instead of analyzing it
Your data pipelines have silent failures no one notices until dashboards go stale
LIMS, EHR, sequencer, and research data live in disconnected systems
Cloud data infrastructure costs are growing faster than throughput or revenue
Audit evidence assembly takes weeks per compliance cycle (HIPAA, CAP, CLIA, SOC 2, GDPR)
Your AI/ML initiatives are stuck in proof-of-concept because data isn’t production-ready
You’re planning a migration from on-prem HPC or legacy LIMS to the cloud
FHIR interoperability with Epic, Cerner, or Athenahealth is a requirement for your business
You handle PHI and need HIPAA-compliant data engineering built into the architecture
Genomic data (VCF, BAM, FASTQ) is stored as flat files with no governance or lineage
If you checked 3 or more boxes, your data infrastructure is likely limiting your growth, compliance, and AI capabilities. Let’s fix that.
Ready to assess where your platform stands?
Schedule Your Free Architecture Assessment →Knowing Spark doesn’t mean knowing how multi-allelic variants split. Or why GRCh38 reference handling breaks naive normalization. Or what FHIR Genomics Reporting actually requires.
| Aspect | Generic Data Engineering | Healthcare Data Engineering (NonStop) |
|---|---|---|
| Data Types | Web logs, transaction data, marketing data | PHI, EHR, claims, LIMS, genomic data (VCF, BAM, FASTQ), medical device streams |
| Compliance | Optional or post-build | Built into architecture: HIPAA, CAP/CLIA, SOC 2, GDPR, 21 CFR Part 11 |
| Standards | Custom APIs, REST, GraphQL | FHIR R4/R5, HL7 v2, SMART on FHIR, GA4GH, US Core |
| Bioinformatics | Not required | Nextflow, Snakemake, WDL, GATK, VCF processing, variant annotation |
| Access Control | Role-based (RBAC) | Attribute-based (ABAC) for PHI authorisation |
| Audit Trails | Nice-to-have | Immutable logs meeting 45 CFR § 164.312(b) for HHS OCR |
| Data Masking | Basic anonymization | Dynamic PHI masking + synthetic data for non-production |
Most engagements include 4–6 disciplines integrated as a cohesive platform. Some are full-platform builds; some are single-discipline modernisations.
Healthcare Data Lakes & Lakehouses
Cloud-native architectures for clinical and genomic data at scale.
- AWS HealthOmics, Azure Synapse, Databricks, Snowflake
- Apache Iceberg, Delta Lake (replacing flat-file VCF)
- Bronze/Silver/Gold medallion patterns
- Unity Catalog, AWS Glue governance
Healthcare ETL Pipelines
Orchestrated, observable, compliant data movement across all clinical systems.
- Apache Airflow, Databricks Workflows, AWS Glue
- dbt, Apache Spark, PySpark, Scala
- FHIR R4 as first-class flow, HL7 v2 normalisation
- LIMS→lake, EHR→warehouse, multi-source integration
Bioinformatics & Genomic Pipelines
Production-grade variant calling, RNA-Seq, single-cell, and annotation.
- Nextflow, Snakemake, WDL, CWL
- GATK HaplotypeCaller, DeepVariant, Mutect2
- STAR, Salmon, Cell Ranger, Alevin
- VEP, gnomAD, ClinVar; NIST GIAB benchmarking
Real-Time Health Data Streaming
Live data from sequencers, lab instruments, EHRs, and wearables.
- Apache Kafka, Apache Flink, AWS Kinesis, AWS MSK
- Sequencer events, lab instrument events, EHR events
- TAT alerting, QC anomaly detection
- Sample lifecycle tracking, real-time dashboards
Clinical Data Integration & Interoperability
FHIR, HL7, EHR integrations standard work, not custom one-off projects.
- HL7 v2 → FHIR R4/R5 translation, FHIR Bulk Data export
- SMART on FHIR v2, CDS Hooks 2.0, US Core 6.1.0
- Epic, Oracle Health Cerner, athenahealth, eClinicalWorks
- Mirth Connect, Rhapsody, Iguana modernisation
HIPAA-Compliant Data Engineering
Compliance built into infrastructure, not documentation.
- HIPAA-aligned VPC, AES-256, TLS 1.2+
- AWS KMS, HashiCorp Vault, ABAC for PHI
- Dynamic PHI masking, tokenisation, synthetic data
- Immutable audit trails 45 CFR § 164.312(b)
Data Quality, Lineage & Observability
No more silent failures; know when data is stale, broken, or drifted.
- Great Expectations, Soda — automated quality gates
- OpenMetadata, Collate, Unity Catalog for lineage
- Monte Carlo, Lightup — freshness and schema drift
- Architecture diagrams, data dictionaries, version control
Data Engineering for AI & ML
Move AI from PoC to production with production-grade feature stores.
- MLflow, Feast, Tecton feature stores
- Databricks, Snowflake ML, SageMaker, Vertex AI
- Synthetic data augmentation for rare populations
- Weights & Biases, model versioning, reproducibility
Every engagement is scoped to your business outcomes: TAT improvement, cost-per-sample reduction, audit cycle time, AI-readiness, query latency, cloud cost optimisation.
Genomics & Bioinformatics Labs
- WGS, WES, panel, and array pipelines
- Variant data management on Iceberg-backed storage
- Spark-native VCF processing via Glow
- Pharmacogenomics, single-cell, federated platforms
- NIST GIAB benchmarking for clinical validation
Clinical Reference & Hospital Labs
- CAP/CLIA-compliant pipelines under 42 CFR Part 493
- Real-time LIMS-to-EHR via HL7 v2 and FHIR R4
- TAT and QC observability dashboards
- FHIR DiagnosticReport output
- De-identified data pipelines for AI/ML
Precision Medicine & Biotech
- Multi-omic data warehouses (genomic, transcriptomic, proteomic)
- Biobank platforms with informed consent tracking
- Drug discovery pipelines
- Phenotypic-genomic harmonization
- Biomarker and target identification infrastructure
Pharma R&D & CROs
- Real-world evidence (RWE) pipelines
- Clinical trial data management
- 21 CFR Part 11 and GDPR-compliant data engineering
- Multi-sponsor data segregation
- Federated architectures for data privacy
Life Sciences SaaS Companies
- Multi-tenant architecture with HIPAA + SOC 2 isolation
- FHIR-native APIs for customer integrations
- Customer-controlled data residency (in-country hosting)
- Enterprise-deal-ready data engineering with SLAs
Cloud-agnostic, but AWS HealthOmics + Apache Iceberg is the most common stack for genomics in 2026.
| Category | Technologies |
|---|---|
| Cloud & Lakehouse | AWS HealthOmics, Azure Synapse, Azure Health Data Services, Google BigQuery, Google Dataflow, Databricks, Snowflake, Microsoft Fabric |
| Storage & Processing | Apache Iceberg, Delta Lake, Apache Spark, PySpark, Scala, Apache Flink, Apache Kafka, dbt, Apache Airflow |
| Bioinformatics | Nextflow, Snakemake, WDL, CWL, GATK4, Mutect2, DeepVariant, STAR, Salmon, VEP, ClinVar, gnomAD, Glow |
| Compliance & PHI | AWS KMS, HashiCorp Vault, Privacera, Anonymeter, custom ABAC frameworks, AES-256, TLS 1.2+ |
| MLOps & AI | MLflow, Feast, Tecton, Weights & Biases, Amazon SageMaker, Google Vertex AI, Azure ML |
| Standards & Interoperability | FHIR R4, FHIR R5, SMART on FHIR v2, HL7 v2, GA4GH, US Core 6.1.0, LOINC, SNOMED, ICD-10 |
Based on an analysis of 12 failure patterns causing healthcare data platform breakdowns and how we engineer past each one.
| Mistake | Consequence | How NonStop Fixes It |
|---|---|---|
| Neglecting data quality | Misdiagnosis, billing errors, AI failures | Automated quality gates (Great Expectations, Soda), real-time monitoring |
| Inadequate data integration | Siloed EHR/LIMS/billing data, incomplete patient profiles | Unified ETL/ELT pipelines, data lake architecture |
| Overlooking scalability | Sluggish pipelines, crashes, rising cloud costs | Modular design, auto-scaling, Iceberg partitioning, storage tiering |
| Insufficient governance | Unauthorized PHI access, regulatory fines | RBAC/ABAC, end-to-end lineage, encryption, regular audits |
| No real-time processing | Delayed alerts, reactive healthcare, stale dashboards | Kafka/Flink streaming pipelines, live instrument/EHR data |
| Ignoring standardization | Inconsistent data, integration pain, incorrect conclusions | Enforced ICD-10/SNOMED/LOINC mapping, data dictionaries |
| Inadequate documentation | Downtime during staff transitions, slow onboarding | Architecture diagrams, pipeline docs, version control, knowledge bases |
| Overcomplicating pipelines | High failure rates, slow troubleshooting | Modular design, single-responsibility components, Airflow orchestration |
| No error handling/monitoring | Silent failures, inaccurate dashboards | Error logs, PagerDuty/Slack alerting, retry logic, SLA dashboards |
| Poor metadata management | Data ownership confusion, redundant pipelines | Apache Atlas, Collibra, Alation — lineage and business definitions |
| Not future-proofing | Costly migrations, inability to adopt AI, vendor lock-in | Open-source tools, schema-on-read, modular cloud architecture |
| Misaligned with business goals | Dashboards no one uses, AI models clinicians don’t trust | Start with clinical problem, define use cases + success metrics with end-users |
Every engagement is outcome-targeted. Hours and resources are inputs. Your business outcomes: TAT, cost-per-sample, audit cycle time, AI-readiness — are the outputs.
Healthcare Data Architecture Review
Best for: Labs planning migration, modernisation, or new platform build
- Current-state assessment across all 8 disciplines
- Compliance gap analysis (HIPAA, CAP/CLIA, SOC 2, GDPR)
- Highest-ROI investment identified
- Phased roadmap with named outcomes
- Realistic scope and budget range for your CFO
- Architecture decision records
Single-Discipline Build
Best for: Specific pain points “our ETL is broken” or “we need FHIR integration”
- Genomic data lake from scratch
- Streaming layer for real-time lab monitoring
- ETL modernisation from legacy to cloud
- FHIR integration with Epic/Cerner
- Bioinformatics pipeline rebuild (Nextflow/GATK)
Multi-Discipline Platform Build
Best for: Series A/B precision medicine companies, lab startups, building from scratch
- 4–6 disciplines integrated as one platform
- Architecture decision records
- SLA dashboards
- Audit-readiness wired into infrastructure
- Phased rollout plan with ongoing support options
Every NonStop healthcare data engineering team includes engineers who have shipped production systems for clinical genomics or life sciences customers.
Domain Depth, Not Just Data Engineering
Knowing Spark ≠ knowing how multi-allelic variants split. Every NonStop engineer has shipped production for clinical genomics or life sciences. No generalists applying data patterns to healthcare.
Compliance Built Into Architecture
HIPAA, CAP/CLIA, SOC 2, GDPR, and 21 CFR Part 11 controls live in Infrastructure as Code and the data platform, not in a slide deck or documentation. SOC 2 readiness comes from how the platform is built, not how it’s documented.
Outcome-Targeted Engagements
Hours and resources are inputs. Your business outcomes are outputs: TAT improvement, cost-per-sample reduction, query latency optimisation, audit cycle time reduction, AI-readiness. Every contract names the output and the measurement methodology.
We Work With Your Stack
Cloud-agnostic: AWS, Azure, GCP. We adapt to your existing infrastructure, compliance requirements, and data residency needs no forced migrations to preferred vendors.
Do you build full data platforms, or just specific layers?
Both. Most engagements scope to 4–6 disciplines. Some are full-platform builds for Series A/B precision medicine companies. Some are single-discipline rebuilds for established labs with specific pain points.
Can you migrate us off on-prem HPC and legacy LIMS to the cloud?
Yes. Migrations to AWS HealthOmics, Databricks, or Snowflake with Apache Iceberg are among our most common engagements. We handle the full migration: assessment, parallel-run period, validation, cutover, and post-migration optimisation.
How do you handle PHI in non-production environments (dev, testing, staging)?
Through dynamic PHI masking and synthetic data generation. Developers and data scientists work with synthetic or masked datasets that preserve statistical properties while not exposing real PHI.
Can you scale to petabyte-scale genomic data (population cohorts)?
Yes. Several NonStop engagements run on cloud lakehouse architectures with sustained processing of population-scale cohorts. A single genomic dataset requires 180–450GB storage with specialised processing consuming 8,000–18,000 CPU hours; we architect for this scale through Iceberg partitioning, Spark execution tuning, storage tiering, and compute optimisation.
What’s the difference between healthcare data engineering and bioinformatics?
Healthcare data engineering focuses on infrastructure: data lakes, ETL pipelines, interoperability (FHIR/HL7), compliance (HIPAA), cloud architecture, real-time streaming. Bioinformatics focuses on biological data analysis: DNA sequences, variant calling, RNA-Seq, protein structures, gene expression. We do both: data engineers who build the infrastructure and bioinformaticians who build production pipelines.
How do you ensure FHIR interoperability with Epic, Cerner, athenahealth?
US Core 6.1.0 conformance + ONC Inferno-validated CI. Epic, Oracle Health Cerner, athenahealth, eClinicalWorks, Meditech, and AllScripts integrations are standard work, not custom projects. FHIR Genomics IG defines profiles for genetic testing requests and results exchange.
What cloud platforms do you support?
All major platforms: AWS (HealthOmics, Glue, Kinesis, MSK), Azure (Synapse, Health Data Services, Data Factory), GCP (BigQuery, Dataflow). We’re cloud-agnostic, but AWS HealthOmics + Iceberg is the most common genomics stack in 2026.
Do you provide data engineering for AI/ML on genomic and clinical data?
Yes. We build production feature stores on Databricks/Snowflake with MLflow/Feast/Tecton. We move AI from PoC to production with training warehouses, feature engineering for omic and clinical datasets, and synthetic data augmentation for rare populations.
Do you provide documentation and knowledge transfer?
Yes. Documentation is non-negotiable: architecture diagrams, pipeline logic, transformation rules, data dictionaries, version control, onboarding guides. We ensure institutional knowledge stays with your organisation.
Can you support multi-tenant SaaS with HIPAA isolation?
Yes. We build multi-tenant architectures with HIPAA and SOC 2 isolation, customer-controlled data residency, and FHIR-native APIs enterprise-deal-ready for life sciences SaaS companies.
45-minute session with a healthcare data engineer who’s shipped production for clinical genomics customers. This is not a sales call you’ll leave with actionable insights even if you never work with us.
What you walk away with
Current-State Map: Assessment across all 8 disciplines: what you have, what’s missing, what’s at risk
Compliance Gap Analysis: HIPAA, CAP/CLIA, SOC 2, GDPR gaps identified against your current architecture
Highest-ROI Investment: Where to start for maximum impact — TAT, cost-per-sample, audit cycle, or AI-readiness
Phased Roadmap: Named outcomes with realistic timelines aligned to your goals
Budget Range: Realistic scope and budget range your CFO can use for planning
Architecture Advice: Actionable recommendations you can execute or bid to other vendors — no lock-in