Data Lake Architect

We are seeking a highly skilled and experienced Data lake Architect to spearhead the definition, design, and governance of data assets within the Supply Chain organization within Global IT. This pivotal role ensures that data remains consistent, high-quality, secure, and accessible to support strategic business initiatives. You will collaborate closely with business stakeholders, delivery leads, data engineers, data scientists, and enterprise architects to build robust data models and integration strategies. Snowflakes DBT cloud Airflow Domain Data Strategy: Translate Supply Chain business goals into a robust, forward-looking data architecture roadmap. You will ensure that our technical design directly solves critical business problems rather than just moving data. Business Logic Modeling: Own the design and lifecycle of the domain’s logical and physical data models. You will ensure complex business rules are accurately codified, versioned, and optimized for analytics consumption in BI reporting tools. Data Product Ownership: Define and manage the domain’s " Data Products ". You will ensure that datasets exposed to the wider enterprise are well-documented, reliable, and standardized (e.g., a "Demand Planning" or "Inventory Levels" or "Customer Service KPIs & measures"). Governance Implementation: Operationalize enterprise-wide standards within your domain. This includes implementing data quality checks, enforcing privacy controls (PII masking), managing metadata/lineage, and ensuring strict adherence to security policies. Solution Optimization & Cost Management: Act as the " Design Authority " for the domain. You will proactively optimize pipeline performance, monitor Snowflake resource consumption (FinOps), and ensure all solutions are architected for long-term scalability and cost-efficiency. What You Will Do Design & Architect: Create comprehensive conceptual, logical, and physical data models for the Supply chain area, utilizing Snowflake best practices (clustering, micro-partitions) to ensure performance. Set Standards: Define the dbt project structure, modeling standards (Kimball/Dimensional), and testing frameworks that the domain's data engineers will build against. Collaborate: Partner with the Data Delivery Lead to ensure architectural integrity during sprint execution, and work with the Central Platform Team to request and configure necessary infrastructure resources. Govern: Implement granular security policies (Row-Level Security, Dynamic Masking) and ensure all data assets are cataloged with clear lineage and metadata. Optimize: Perform regular reviews of high-cost queries and data pipelines; re-architect inefficient flows to reduce Snowflake credit consumption and improve report performance. Evaluate: Assess new features (e.g., Iceberg tables) and tools to determine their fit for the domain’s evolving needs. What You Bring to the Table Technical Expertise Data Modeling: Expert-level proficiency in dimensional data modeling (Star Schema, Snowflake Schema) and ability to map complex business processes to data structures. Experience with data modeling & diagramming tools. Modern Data Stack: Deep hands‑on experience with Snowflake or any cloud data warehouse (architecture, performance tuning, security) and dbt or similar SQL based data transformation tools (project structure, macros, testing, documentation). Orchestration & Infrastructure: Solid understanding of Airflow or other orchestration tools DAG design patterns and dependencies. Familiarity with Infrastructure-as-Code principles (Terraform or similar) and Containerization concepts is a plus. Analytics Integration: Understanding of how BI tools ( Domo, Sigma or similar) ingest and query data, with the ability to design models that ensure fast, interactive dashboard performance. Professional Skills Strategic Thinking: Ability to see the "big picture" and design systems that persist beyond the current sprint. Communication: Excellent ability to translate technical concepts into business language for stakeholders and vice-versa. Domain Knowledge: Prior experience in Supply Chain data is highly preferred. Qualifications Education: Bachelor's or Master's degree in Computer Science, Information Systems, Data Science, or a related field. Experience: 6+ years of experience in data architecture, data modeling, and data management. Technical Knowledge Knowledge of data warehousing/data lakes (e.g., Snowflake, Databricks, GCP BigQuery, Hadoop). Experience with transformation tools like DBT or Coalesce. Understanding of cloud platforms (GCP, AWS, Azure). Deep understanding of modeling methodologies (relational, dimensional, NoSQL). Strong grasp of integration patterns (ETL, ELT, API integrations). Experience with relational databases (e.g., SQL, SAP HANA, SAP BW). Enterprise relational platform & process knowledge is a plus. Governance Governance: Familiarity with data governance principles, data quality management tools, and practices. Skills: Excellent communication, presentation, interpersonal, and problem-solving skills. #J-18808-Ljbffr Bristlecone