Responsibilities

Complete conceptual, logical and physical data models for any supported platform, including SQL Data Warehouse, EMR, Spark, DataBricks, Snowflake, Azure Synapse or other Cloud data warehousing technologies.
Governs data design/modeling – documentation of metadata (business definitions of entities and attributes) and constructions database objects, for baseline and investment funded projects, as assigned.
Provides and/or supports data analysis, requirements gathering, solution development, and design reviews for enhancements to, or new, applications/reporting.
Supports assigned project contractors (both on- & off-shore), orienting new contractors to standards, best practices, and tools.
Contributes to project cost estimates, working with senior members of team to evaluate the size and complexity of the changes or new development.
Ensure physical and logical data models are designed with an extensible philosophy to support future, unknown use cases with minimal rework.
Develop a deep understanding of the business domain and enterprise technology inventory to craft a solution roadmap that achieves business objectives, maximizes reuse.
Partner with IT, data engineering and other teams to ensure the enterprise data model incorporates key dimensions needed for the proper management: business and financial policies, security, local-market regulatory rules, consumer privacy by design principles (PII management) and all linked across fundamental identity foundations.
Drive collaborative reviews of design, code, data, security features implementation performed by data engineers to drive data product development.
Assist with data planning, sourcing, collection, profiling, and transformation.
Create Source To Target Mappings for ETL and BI developers.
Show expertise for data at all levels: low-latency, relational, and unstructured data stores; analytical and data lakes; data str/cleansing.
Partner with the Data Governance team to standardize their classification of unstructured data into standard structures for data discovery and action by business customers and stakeholders.
Support data lineage and mapping of source system data to canonical data stores for research, analysis and productization.

Qualifications

8+ years of overall technology experience that includes at least 4+ years of data modeling and systems architecture.
3+ years of experience with Data Lake Infrastructure, Data Warehousing, and Data Analytics tools.
4+ years of experience developing enterprise data models.
Experience in building solutions in the retail or in the supply chain space.
Expertise in data modeling tools (ER/Studio, Erwin, IDM/ARDM models).
Experience with integration of multi cloud services (Azure) with on-premises technologies.
Experience with data profiling and data quality tools like Apache Griffin, Deequ, and Great Expectations.
Experience building/operating highly available, distributed systems of data extraction, ingestion, and processing of large data sets.
Experience with at least one MPP database technology such as Redshift, Synapse, Teradata or SnowFlake.
Experience with version control systems like Github and deployment & CI tools.
Experience with Azure Data Factory, Databricks and Azure Machine learning is a plus.
Experience of metadata management, data lineage, and data glossaries is a plus.
Working knowledge of agile development, including DevOps and DataOps concepts.
Familiarity with business intelligence tools (such as PowerBI).

Architect - Enterprise Data Operations