Data Architect

Careers Signant Health

About The Position

The Data Architect will define and lead the enterprise data architecture and strategy that brings data from Signant Health's diverse operational systems into Snowflake, our primary enterprise data platform. As an individual contributor reporting to the Head of AI, this role combines strategic architectural thinking with hands-on data engineering — building the frameworks and reference pipelines that engineering teams adopt and scale. This is an opportunity to break down data silos, reduce redundant integrations, and enable data-driven decision making and AI across the organization.

Requirements

10+ years in data engineering or software engineering, including 5+ years in data architecture and 3+ years delivering production Snowflake solutions.
Deep experience integrating heterogeneous sources (SQL Server, Oracle, PostgreSQL, DynamoDB, MongoDB, enterprise SaaS); expert SQL and data modeling skills; proficiency in Python and dbt.
Proven AWS experience (S3, Glue, Lambda, Step Functions, IAM); experience with orchestration, CI/CD, and infrastructure-as-code (Airflow, Terraform, Git).
Experience designing governance, security, and compliance controls for regulated data; ability to produce architecture artifacts and influence stakeholders without direct authority.
Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent experience.

Nice To Haves

Experience in pharmaceutical, clinical research, or another regulated industry (GxP, 21 CFR Part 11, HIPAA, GDPR, GAMP 5); familiarity with CDISC SDTM/ODM or HL7 FHIR.
Advanced Snowflake experience (Snowpark, Dynamic Tables, Iceberg, Cortex AI, Secure Data Sharing); experience with AI/ML enablement, data catalog/observability tools, or data mesh patterns.
SnowPro Advanced certification; AWS data or solutions architecture certifications a plus.

Responsibilities

Define and maintain the target-state enterprise data architecture and multi-year roadmap, with Snowflake as the central data platform.
Document the current-state data landscape across SQL Server, Oracle, PostgreSQL, DynamoDB, MongoDB, and enterprise SaaS applications; perform gap analysis and develop a prioritized onboarding roadmap.
Establish architectural principles, reference architectures, and standard patterns for data ingestion, storage, transformation, and consumption; evaluate and recommend tooling via build-vs.-buy analysis.
Design and build ingestion patterns — CDC, batch, streaming, and API-based — using AWS-native and Snowflake-native services (AWS DMS, Glue, Kinesis/MSK, Snowpipe, dbt) and establish engineering standards for pipelines, CI/CD, observability, and error handling.
Develop source-to-target reconciliation and validation frameworks consistent with ALCOA+ data integrity principles; optimize Snowflake performance and cost as data volumes grow.
Design enterprise and canonical data models and a layered Snowflake architecture (raw, conformed, and curated/medallion layers), including conformed business entities shared across products.
Establish master and reference data management approaches; guide selection of dimensional, Data Vault, or other modeling techniques appropriate to each use case.
Define the enterprise data governance framework including ownership, stewardship, data contracts, quality monitoring, metadata management, and end-to-end data lineage.
Design data protection controls (classification, RBAC, masking, tokenization of PII/PHI) and ensure the architecture supports GxP, 21 CFR Part 11, HIPAA, GDPR, and SOC 2 requirements.
Partner with BI, analytics, and AI teams to deliver curated data products for reporting, self-service analytics, and AI/ML use cases including RAG pipelines and Snowflake Cortex AI.
Lead cross-team architecture reviews, mentor engineers, and serve as the primary technical liaison with Snowflake and other data platform vendors.