Enterprise Data Operations Analyst

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 streaming (consumption/production), data in-transit.
• Develop reusable data models based on cloud-centric, code-first approaches to data management and 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.
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Qualifications

• 5+ years of overall technology experience that includes at least 2+ years of data modeling and systems architecture.
• Around 2+ years of experience with Data Lake Infrastructure, Data Warehousing, and Data Analytics tools.
• 2+ 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).