Technical Architect - Snowflake (1317)

Axtria helps leading life sciences companies modernize their commercial data and AI platforms. Our AI & Data practice is growing its Snowflake capability to support large-scale programs that move legacy cloud and on-premise data stacks to modern, governed, AI-ready commercial data platforms serving brand analytics, field reporting, incentive compensation, and downstream consuming applications.

We are looking for a hands-on Technical Architect to own the target-state architecture, lead phased migration programs, and ensure the platform is stable, high-quality, and ready for agentic and GenAI use cases. You will work alongside client data and technology leaders, Axtria delivery leads, and onshore/offshore engineering teams.

• Own end-to-end reference architecture for enterprise commercial data platforms on Snowflake, covering ingestion, layered data modeling (raw, curated, consumption), security, cost governance, and environment strategy.
• Design and lead migration of workloads from legacy platforms (e.g., AWS S3/EMR/EKS/Redshift, Hadoop) to Snowflake, including Spark/PySpark job refactoring, Redshift SQL conversion, and parallel-run and reconciliation approaches.
• Define patterns for onboarding commercial data feeds (IQVIA, Symphony, specialty pharmacy, claims, CRM/Veeva, payer, and MDM outputs), with reusable frameworks that shorten new feed and new product onboarding.
• Architect data quality and observability capabilities (rules, anomaly detection, release gating, lineage) in partnership with data governance and data quality teams.
• Establish Snowflake best practices for RBAC, secure data sharing, masking and row-level security, warehouse sizing, and FinOps/cost monitoring.
• Design orchestration and CI/CD (Airflow or equivalent, dbt, Git-based DevOps, infrastructure as code) for reliable, auditable releases.
• Enable AI readiness on the platform: semantic and context layers, metadata and data dictionary services, Snowflake Cortex/Snowpark use cases, and integration points for agentic operations (feed monitoring, change request automation, DQ triage).
• Partner with downstream and application teams to plan cutover and minimize disruption to consuming systems and reports.
• Act as technical authority in client architecture forums, review boards, and executive updates; produce clear architecture decision records, design documents, and roadmaps.
• Mentor engineers and architects across onshore and offshore teams; support change management and Snowflake enablement for client audiences.

Snowflake

• Snowflake Platform: Deep expertise in Snowflake architecture: virtual warehouses, multi-cluster scaling, micro-partitioning, clustering keys, search optimization, query acceleration, result caching, and Time Travel/Fail-safe.
• Ingestion & Processing: Snowpipe and Snowpipe Streaming, Streams and Tasks, Dynamic Tables, external and Iceberg tables, stages, and file formats for batch and near-real-time ingestion.
• Sharing & Resilience: Secure data sharing, Data Clean Rooms, Snowflake Marketplace, replication and failover, and cross-region/cross-cloud strategies.
• Snowpark & AI: Snowpark (Python/Java/Scala), UDFs/UDTFs, stored procedures, Snowflake Cortex (LLM functions, Cortex Search, Cortex Analyst), and Streamlit in Snowflake.
• Security & Governance: RBAC/role hierarchy design, network policies, SSO/SCIM, key-pair auth, dynamic data masking, row access policies, object tagging, and Horizon governance features.
• Performance & FinOps: Resource monitors, warehouse right-sizing, query profiling and tuning, workload isolation, and credit consumption chargeback models.

Data Engineering

• Languages: Advanced SQL (window functions, semi-structured data with VARIANT/FLATTEN), Python, and PySpark; working knowledge of Scala or Java is a plus.
• Transformation: dbt (models, macros, tests, snapshots, packages), ELT/ETL design, and tools such as Fivetran, Matillion, Informatica, or AWS Glue.
• Orchestration: Apache Airflow/MWAA, Step Functions, or equivalent; dependency management, SLA monitoring, retries, and backfill strategies.
• Data Modeling: Dimensional modeling (Kimball), Data Vault 2.0, medallion/layered architectures, slowly changing dimensions, and late-arriving data handling.

Cloud, DevOps & Tooling

• Cloud: AWS: S3, EMR, EKS, Redshift, Glue, Lambda, IAM, KMS, VPC/PrivateLink, CloudWatch; Azure or GCP exposure is a plus.
• DevOps & IaC: Git, GitHub Actions/Jenkins/Azure DevOps, Terraform (including Snowflake provider), schemachange or similar, and environment promotion strategies.
• Quality & Observability: Data quality frameworks (Great Expectations, Soda, dbt tests), observability (Monte Carlo or similar), data catalogs and lineage (Alation, Collibra, Atlan, or OpenLineage).
• Consumption: Integration with BI and consuming tools such as Tableau, Power BI, Qlik, and downstream applications via APIs and reverse ETL.

• Translate business and analytics requirements into target-state data architectures, logical and physical designs, and phased roadmaps.
• Assess current-state platforms, identify technical debt and fragility, and define migration strategies (lift-and-shift, re-platform, re-architect) with clear risk, cost, and timeline trade-offs.
• Design for non-functional requirements: scalability, availability, performance SLAs, disaster recovery, security, auditability, and regulatory compliance (HIPAA, GxP, SOX where applicable).
• Define reusable reference patterns and accelerators for ingestion, transformation, data quality, metadata, and consumption that teams can adopt consistently.
• Establish architecture governance: design standards, architecture decision records (ADRs), review boards, and conformance checks across delivery teams.
• Build TCO and cost models, compare platform and tooling options (e.g., Snowflake vs. Databricks, native vs. third-party tools), and present recommendations to senior stakeholders.
• Design semantic layers, metadata models, and context layers that make enterprise data usable by BI tools, GenAI applications, and AI agents.
• Plan cutover, parallel-run, reconciliation, and legacy decommissioning with minimal disruption to downstream consumers.
• Produce clear architecture artifacts (context, component, data flow, and deployment diagrams) and communicate them effectively to both technical and executive audiences.
• Guide estimation, solutioning, and proposal responses for new opportunities, contributing to practice growth and pre-sales.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 12+ years in data engineering and architecture, with 4+ years designing and delivering enterprise solutions on Snowflake.
• Proven experience leading at least one large-scale migration from Hadoop/EMR, Redshift, or similar platforms to Snowflake.
• Strong command of SQL, Python/PySpark, data modeling (dimensional and Data Vault), and ELT frameworks such as dbt.
• Hands-on AWS experience (S3, EMR, EKS, IAM, Glue, Lambda) and an understanding of how to decommission legacy components safely.
• Experience with orchestration (Airflow, MWAA, or similar), CI/CD, and infrastructure as code (Terraform).
• Solid grounding in data governance, data quality, lineage, and security in regulated environments.
• Excellent communication skills, with the ability to explain architecture trade-offs to senior client stakeholders and engineering teams alike.

• Pharma or life sciences commercial data experience: sales and claims data, specialty data, customer and product master data, field reporting, incentive compensation.
• Experience with Snowflake Cortex, Snowpark, Streamlit, or building semantic layers for GenAI and agentic use cases.
• Familiarity with data observability tools (Monte Carlo, Great Expectations, Soda, or similar).
• SnowPro Advanced: Architect or Data Engineer certification; AWS certifications are a plus.
• Experience working in onshore/offshore delivery models within a consulting environment.

You will shape high-impact commercial data modernization programs in the life sciences industry, working at the intersection of cloud data platforms, data quality, and AI, with a team that brings deep, long-standing domain expertise.

Pay Transparency Laws 
Salary range or hourly pay range for the position 
 
The salary range for this position is $131,200 to $174,975 annually. The actual salary will vary based on applicant’s education, experience, skills, and abilities, as well as internal equity and alignment with market data. The salary may also be adjusted based on applicant’s geographic location. 
 
The salary range reflected is based on a primary work location of Berkeley Heights, New Jersey. The actual salary may vary for applicants in a different geographic location. 

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