Senior Data Engineer

Precision AQ is building a centralized Data Hub to consolidate fragmented data infrastructure, establish enterprise-wide data governance, and enable AI-ready analytics across our life sciences portfolio. This is a foundational initiative, not a maintenance role.
We are hiring a Senior Data Engineer to own the development of our Bronze and Silver data layers, the ingestion, transformation, and enrichment pipelines that turn raw healthcare data into governed, reliable, and reusable data assets ready for business use.
This role exists because we need engineers who can build at the intersection of healthcare data complexity and modern data engineering best practices. You will work directly with claims, formulary, and other critical data sources, building the pipelines that feed every downstream analytics use case, product, and client deliverable.
You will report to the Engineering Director and work closely with the Enterprise Data Architect to translate architecture patterns into production-grade pipelines. This is a hands-on technical role with significant autonomy. If you want to shape how a growing healthcare and life sciences company builds its data foundation, this is the opportunity.
Main

Duties & Responsibilities
Build and Maintain Bronze/Silver Layer Pipelines:
You will ensure core data sources lands accurately, on time, and with full lineage.
Lead Data Ingestion, Transformation, and Enrichment:
You will own the end-to-end pipeline from raw file landing through cleansed, conformed staging tables, including deduplication, standardization, code mapping, and entity resolution.
Develop Automated Ingestion Pipelines:
You will use Snowpipe, Matillion, or custom solutions with reliability, observability, and minimal manual intervention in mind.
Implement dbt Models:
You will write clean, tested, documented SQL that follows medallion architecture patterns and supports incremental processing ablish Scalable Engineering Patterns:
You will create reusable templates, macros, and testing frameworks that accelerate onboarding of new data sources without sacrificing quality.
Ensure Data Quality and Observability:
You will implement dbt tests, freshness checks, and anomaly detection. You will own the pipeline reliability and respond to data quality incidents.
Collaborate with the Architect and Stakeholders:
You will translate business requirements into technical specifications, participate in design reviews, and contribute to ADRs, and you will ensure pipelines align with enterprise standards.

Education And Experience

Education:

Bachelor’s degree in computer science, information systems, engineering, or a related technical field. Advance degree is a plus but not required.
Required
5+ years of professional data engineering experience, with at least 3 years working in modern cloud data environments
2+ years hands-on experience with Snowflake, including performance tuning, Snowpipe, and Snowflake-specific optimization patterns
2+ years working with dbt (Core or Cload) in production environments, including incremental models, testing, documentation, and package management
Demonstrated experience with healthcare data. You understand ICD-10, NPI, NDC codes, and the messy realities of claims data quality
Production experience with orchestration tools, such as Airflow, Dagster, or similar. You’ve built and maintained DAGs that run reliably at scale
Strong SQL skills. You write efficient, readable, well-structured SQL and can debug complex query performance issues
Python proficiency for data processing, scripting, and pipeline automation

Preferred

Experience with major claims data aggregators (e.g., McKesson, Symphony Health, IQVIA, Komodo)
Familiarity with formulary and payer data
Experience implementing medallion architecture or similar layered data patterns
Background in life sciences, pharma services, or healthcare analytics organizations

Experience with data observability tools
Exposure to Snowpipe, Matillion, Fivetran, or other managed ingestion solutions
Familiarity with Git-based workflows, CI/CD for data pipelines, and infrastructure-as-code

Knowledge, Skills, And Competencies
Technical Stack and Environments:
Data Platforms…