Data Architect

About the Role

Reporting to the Manager of Data Analytics and IT, the architect is responsible for data architecture, data quality, and platform reliability, embedding data cleansing, normalization, validation, and observability into every pipeline. The role works closely with analytics, finance, operations, and IT leadership to ensure data correctness and consistency for AI outcomes. The objective is to build and maintain a governed Snowflake + Databricks data platform where data quality, lineage, KPI consistency, and observability are enforced by design, enabling reliable analytics and scalable AI adoption across the organization. Collaboration with data engineers, analysts, and IT teams is essential to deliver production‑ready data assets, enforce governance, security, and masking policies, and provide transparent lineage from source systems to analytics and AI outputs. • Architect and maintain Snowflake as the enterprise analytical and AI data backbone, including schema design for KPIs, self‑service analytics, and AI datasets. • Implement and govern Snowflake features such as Streams, Tasks, Dynamic Tables, Snowpark, and secure data sharing while optimizing warehouse sizing and cost. • Reverse‑engineer Databricks as the processing, cleansing, and enrichment layer, defining Bronze, Silver, and Gold medallion architecture. • Design Spark‑based cleansing logic for deduplication, standardization, schema enforcement, null handling, outlier detection, and anomaly flagging. • Build reusable data quality, completeness, and consistency frameworks and automate quality checks at each pipeline stage. • Create governed Snowflake views that centralize KPI logic for reporting, dashboards, and AI workloads. • Design and oversee ELT pipelines from ERP, SaaS, and APIs using Fivetran, dbt, and Python, embedding validation and reconciliation. • Develop monitoring, alerting, and anomaly detection for Snowflake and Databricks pipelines, covering failures, SLA breaches, data freshness, and schema changes. • Enable AI and LLM data workflows by preparing Retrieval‑Augmented Generation (RAG) datasets, embedding generation, and supporting Snowflake Cortex and Azure OpenAI integrations. • Enforce data governance, access controls, masking, and lineage to ensure transparency from source systems to analytics and AI outputs. • Collaborate with analytics, finance, operations, and IT leadership to align data assets with business objectives.

Key Responsibilities

• ▸snowflake architecture
• ▸databricks architecture
• ▸spark cleansing
• ▸elt pipelines
• ▸data governance
• ▸observability

What You Bring

Required qualifications include 6+ years of experience in data architecture or engineering, strong hands‑on Snowflake and Databricks/Apache Spark expertise, advanced SQL and Python skills, and cloud experience (Azure preferred). Preferred qualifications add experience with Snowflake Cortex, Snowpark, RAG pipelines, embeddings, vector search, and support for finance/ERP multi‑entity data environments. • Required: 6+ years in data architecture/engineering, strong Snowflake and Databricks/Apache Spark experience, advanced SQL, Python, and Azure cloud expertise. • Preferred: experience with Snowflake Cortex, Snowpark SQL/AI functions, RAG pipelines, vector search, and finance/ERP multi‑entity data environments.

Requirements

• ▸snowflake
• ▸databricks
• ▸azure
• ▸sql
• ▸python
• ▸6+ years

Work Environment

Office Full-Time