Lead & Senior Data Engineer

Virtasant•Austin, TX

4h•Remote

About The Position

Virtasant is hiring four Lead and Senior Data Engineers (two at each level) for a multi-month engagement that is likely to extend. The team will support a large enterprise program in healthcare operations and workforce decisioning. The platform integrates data from hospital systems, maps it to a shared ontology and knowledge graph, and helps executives make operational decisions, take action, and measure results. The engineers will build repeatable connectors and pipelines that bring data from HR, scheduling, EHR, and external benchmark sources into Snowflake on Azure, map the data to the ontology, and publish it to a Neo4j graph. The goal is to make new-customer data onboarding fast and repeatable. The work involves sensitive client data and routine use of AI-assisted development. Both levels are hands-on and write production code weekly. Lead engineers own a pod’s technical direction, design, standards, and work priorities; work directly with client architects and data owners on source extracts, data contracts, and acceptance criteria; review code and designs; and remain accountable for the pod’s deliverables. Senior engineers independently own substantial components end to end within the Lead’s direction, raise issues early, and work without day-to-day supervision. The role is remote within the United States. Candidates must be based in the US and authorized to work there. Occasional domestic travel may be required. The anticipated start date is around December 1, 2026.

Requirements

Production experience integrating messy enterprise source systems, with the ability to discuss handling late files, schema drift, duplicates, and conflicting records.
Strong SQL and Python skills.
Hands-on Snowflake experience.
Experience building and operating layered data pipelines with dbt or an equivalent, keeping mappings and rules in versioned code.
Experience with data contracts and versioned schemas, including arrival validation, quarantine of failed data, and safe schema evolution.
Experience incorporating data quality, lineage, reconciliation, and idempotent reruns into data pipelines.
Experience working with regulated or sensitive data under PHI/PII controls such as HIPAA, including masking or tokenisation, least-privilege access, and audit trails.
Experience building multi-tenant data platforms with strict per-customer isolation and repeatable onboarding.
Azure experience with storage, identity, and infrastructure as code using Terraform or Bicep.
Daily use of coding assistants and the ability to check their output.
Must be based in the United States and authorized to work in the US.
Available to start around December 1, 2026.
Lead-level candidates must have led a small engineering team, set technical direction, and worked directly with client architects and stakeholders.

Nice To Haves

Healthcare data experience with workforce systems such as Workday or UKG, scheduling and timekeeping systems, or EHRs such as Epic, Cerner, or MEDITECH.
Graph database experience, ideally Neo4j, including loading a graph from a warehouse and modelling data for a graph.
Experience mapping data into an ontology or common data model shared across customers.
Entity resolution experience matching people and records across systems without a shared identifier.
Experience with external benchmark or reference data feeds delivered by data share or flat file.
Experience building batch pipelines to be event-ready, with idempotent loads and incremental refresh.
Familiarity with open lineage and metadata standards such as OpenLineage.
Experience monitoring production data feeds for freshness, volume, and failures.

Responsibilities

Build production connectors and data pipelines for HR, scheduling, EHR, and external benchmark sources into Snowflake on Azure.
Map source data to a shared ontology and publish it to a Neo4j knowledge graph.
Make customer data onboarding repeatable so new customers can be configured and mapped rather than requiring a new codebase.
Build and operate layered pipelines, such as raw, cleaned, and modelled data layers, using dbt or an equivalent.
Define and version data contracts and schemas, validate incoming data, quarantine invalid data, and evolve schemas without breaking downstream systems.
Implement data quality, lineage, and reconciliation so source totals can be verified, records traced, and loads rerun without duplication.
Work with sensitive client data under applicable PHI/PII controls, including masking or tokenisation, least-privilege access, and audit trails.
Use coding assistants as part of development and review their output.
For Lead-level roles, set pod technical direction and standards, prioritize work, coordinate with client architects and data owners, review work, and be accountable for pod deliverables.
For Senior-level roles, independently deliver significant components such as source connectors, mappings, or domain reconciliation.
Travel domestically occasionally if required.