Before a data pipeline is built, someone has to decide what the data platform should be — how it's structured, how it scales, how it stays compliant, and how every downstream team can trust it. At H3Tech, that decision is made in partnership with the client, and you're accountable for the technical half of it.

H3Tech (Healthcare, High-tech, Human) partners with HealthTech companies, insurers, providers, and life sciences organizations to build software that serves clinicians, patients, and administrators. Fully remote, AI-era engineering; with people making every decision that matters.

The Role

You own the data architecture for H3Tech's most complex client engagements - from the first day of Discovery through delivery. Your architecture decisions and the delivery team's data requirements converge into a single platform design the entire engagement builds from. You also set the standards that apply across H3Tech's data engineering practice and make the calls that sit above what a Senior Data Engineer can resolve.

What You're Responsible For

Lead the data architecture track of Discovery: platform design, technology selection, and assessment of existing client data systems
Co-own the specification baseline — your data platform design and the delivery requirements produce one artifact the team commits to
Define and enforce data modeling standards (Kimball, Data Vault, or hybrid) appropriate to each client context
Produce and maintain architecture decision records (ADRs) that explain what was decided, what was rejected, and why
Set pipeline security, governance, and compliance posture: access control, data lineage, PII handling, audit architecture
Advise Senior Data Engineers on architecture standards and cross-engagement technical questions throughout delivery
Identify and flag data-layer risk at design time — scalability, compliance, integration, observability — before the build starts
Own the data engineering hiring bar and practice standards; build the data capability across H3Tech

Qualifications

Required
- Bachelor's or above in IT, computer science, or related; master's or equivalent depth preferred
- 7+ years in data engineering or data architecture, with at least 3 years in a senior or lead capacity owning platform-level decisions
- Hands-on engineering capability: able to contribute at Senior Data Engineer level when the engagement requires it, not just design from a distance
- Has assessed an existing client data system and produced an actionable technical baseline from it
- Comfortable reviewing and steering AI-generated code at volume; able to catch correctness, security, and quality issues at pace
- Business-level English - strong enough to own architecture conversations with client CTOs and technical leadership
Technical Skills
- Data lakehouse architecture: Delta Lake, Iceberg, or Hudi — design trade-offs, not just implementation
- Cloud data platforms: AWS (S3, Glue, Athena, Redshift, Lake Formation) and/or Snowflake at design and performance-tuning depth
- Infrastructure-as-code: Terraform, Pulumi, or equivalent — can design and review, not just author
- Data governance and security: lineage, access control, PII classification, compliance-aware architecture from day one
- Orchestration and observability: Airflow, dbt, or equivalent — can evaluate and recommend across options, not just use one
Strong Advantage
- Multi-cloud or hybrid cloud data architecture: AWS + Snowflake + Azure or GCP in the same engagement
- ML pipeline infrastructure: feature stores, model registries, data versioning — how data architecture supports AI/ML at scale
- Has independently owned both the data architecture design and the delivery commitment on the same engagement
Domain & Compliance
- Deep knowledge of at least one regulated data domain: healthcare (HIPAA, HL7, FHIR), pharmaceutical (21 CFR Part 11), financial services, or equivalent

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