Data Architect Jobs

About The Position

Requirements

• Bachelor’s degree in Computer Science, Engineering, Statistics, Information Technology, Bioinformatics or Technical Field.
• Minimum of 5 years of hands-on data engineering experience.
• Minimum of 5 years of applied expertise across Python, SQLJava, Spring, Spring Boot, Prefect, and/or other business intelligence tools, , ETL/ELT pipelines, and cloud platforms (AWS Glue/EMR, Snowflake, or Databricks), — applied directly to real-world healthcare data at scale.
• Minimum of 3 years of direct people management experience, including leading, coaching, and developing team members.

Nice To Haves

• Master’s degree in Computer Science, Engineering, Statistics, Information Technology, Bioinformatics.
• Minimum of 5 years of hands-on data engineering experience. with a focus on healthcare or life sciences RWD Preferred.
• Experience with distributed computing frameworks (Spark, Dask) for large-scale RWD processing.
• Advanced SQL optimization skills for AWS Redshift and/or S3/Databricks-based architectures, including query tuning and workload management.
• Deep understanding of healthcare coding standards (ICD-10, NDC, RxNorm, SNOMED CT, CPT, LOINC), real-world data structures, and major RWD vendors and platforms (Truveta, Optum, IQVIA, Komodo, HealthVerity).
• Familiarity with DevOps and CI/CD practices relevant to data pipeline development and deployment.
• Strong problem-solving skills, attention to detail, and ability to work effectively in matrixed, cross-functional teams with both technical and scientific partners.
• Demonstrated ability to build production-grade ingestion pipelines for multi-terabyte, multi-modal datasets: claims, complex her.
• Experience with OMOP CDM transformation, vocabulary management (Athena), and federated analytics networks (OHDSI, PCORnet, Sentinel).
• Familiarity with bioinformatics workflow managers (Snakemake, Nextflow, WDL) and genomics cloud platforms (DNAnexus, Terra, AWS Genomics CLI).
• Experience with data pipeline observability and quality tooling (dbt, Great Expectations, Monte Carlo).
• Familiarity with FDA RWE Framework guidance, EMA RWD guidance, and audit-readiness requirements for observational studies.
• Knowledge of Agile/Scrum methodologies and project management tools such as JIRA
• Knowledge and experience with –omics RWD: Genomics (e.g., ingesting and processing VCF/GVCF files, PLINK binary formats (bed/bim/fam), Transcriptomics (e.g., bulk RNA-seq count matrices (featureCounts, STAR/RSEM), single-cell and single-nucleus RNA-seq (AnnData/h5ad, 10x Genomics CellRanger) — ingestion, normalization, and linkage to clinical phenotype data; Proteomics (e.g., DIA/DDA mass spectrometry output (MaxQuant, DIA-NN) and OLINK).
• Recruitment open across sectors: pharma/biotech data teams, RWD vendors (Truveta, Komodo, Optum, IQVIA, HealthVerity), cloud providers (AWS, Google, Microsoft), genomics platforms (Illumina, DNAnexus, Broad Institute), or academic health systems with large-scale RWD programs.

Responsibilities

• Lead the design, development, and implementation of cloud-native data products and high-throughput data pipelines that transform raw real-world data into scalable, reliable, analysis-ready assets supporting analytics, reporting, and evidence generation.
• Lead the Analytic Data Products Strategy to deliver key data assets that enable streamlined, compliant execution and analytics.
• Own the end-to-end lifecycle of RWD data products, from requirements gathering and prototyping through production deployment and optimization, ensuring scalability, reliability, performance, and reproducibility across cloud environments (e.g., Databricks, AWS S3, Azure Data Lake).
• Build, optimize, and maintain ETL/ELT ingestion and transformation pipelines for large-scale, multi-modal RWD — including claims, complex EHR data, and other linked healthcare datasets — handling data volumes ranging from tens of millions to billions of records.
• Implement and manage lakehouse-style data architectures (e.g., medallion bronze/silver/gold patterns) using Databricks and cloud object storage (AWS S3, ADLS) to produce versioned, partitioned, and audit-ready data assets.
• Write and maintain reusable, version-controlled transformation logic incorporating healthcare coding and terminology standards (e.g., ICD-10/ICD-9, NDC, RxNorm, SNOMED, CPT/HCPCS, LOINC) to produce domain-level datasets such as demographics, diagnoses, treatments, procedures, encounters, and labs.
• Optimize SQL and distributed processing workloads (e.g., Spark-based jobs) for performance across very large datasets, applying partitioning, indexing, predicate pushdown, denormalization, and other optimization strategies appropriate to analytical workloads.
• Translate analytic, business, and research requirements into reproducible data extraction and transformation logic, supporting cohort construction, temporal logic, and consistent reuse of RWD across teams.
• Apply deep understanding of healthcare data structures and standards when engineering data products, ensuring datasets are fit for purpose for downstream analytics and compliant with scientific, regulatory, and audit expectations.
• Establish and implement standard engineering practices and methodology across the data asset lifecycle, including automated data ingestion, data quality checks, integrity testing, validation, monitoring, alerting, and documentation from source table to analysis-ready output.
• Lead CI/CD pipeline setup, code review, and testing standards, ensuring all transformation code is version-controlled, tested, and deployable in a reproducible manner.
• Collaborate closely with multi-functional partners — data scientists, statisticians, analytics leaders, and other technical teams — to understand business and technical requirements and develop documentation of RWD engineering standards, transformation templates, code list repositories, and pipeline performance guidelines.
• Provide technical consultation to collaborators on appropriate use of data products and underlying RWD assets, including structural limitations of specific data sources, join strategies, and performance considerations; develop source-specific training materials for HEOR scientists, SDIA, and statisticians.
• Develop and implement KPIs to measure system performance, efficiency and pull through to program impact.
• Create an inclusive culture where producing and maintaining high-quality data is a core discipline.

Benefits

• Relocation assistance is available for qualified candidates.
• company bonus (depending, in part, on company and individual performance).
• company-sponsored 401(k)
• pension
• vacation benefits
• medical, dental, vision and prescription drug benefits
• flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts)
• life insurance and death benefits
• certain time off and leave of absence benefits
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).