Senior Data Solutions Analyst

Senior Data Solutions Analyst

Requirements

• Bachelor’s or master’s degree in information management, analytics, data science, mathematics, statistics, business administration, or related discipline.
• 2+ years proven experience in data analysis, management, or stewardship roles working with large datasets.
• Proficient in technology stack. Technology stack includes – Azure, SQL, Snowflake, Databricks, PySpark.
• Strong analytical, creative problem-solving and decision-making skills.
• Strong interpersonal, collaboration, communication, and storytelling skills.
• Ability to communicate technical concepts to technical and non-technical audiences.
• Able to understand cloud architecture, components and services and collaborate with Engineers.
• Passionate about data and technical documentation.

Responsibilities

• Be comfortable working in different technologies, flexing when needed (Azure, Snowflake, Databricks, Hadoop, API’s, Tableau, PowerBI, Grafana).
• Have strong business and storytelling acumen to understand and support current and future strategy needs.
• Enjoy working in an exploratory role with the ability to drive for results in ambiguity.
• Be comfortable thinking outside the box or creatively problem solving.
• Own connection and relationship back to data source stakeholders/vendor (internal and/or external).
• Possess deep knowledge and understanding of your data assets and domain.
• Advise, alert and resolve data issues and discrepancies to provide support to internal teams and external clients using the data.
• Develop deep understanding of the business domain, business context and products that use your data.
• Identify opportunities for process improvement and innovation in data management practices.
• Collaborate on data discovery for new/existing data (internal, Kroger, 3rd party, etc.), including data queries/analysis using various tools (e.g. SQL, PySpark, BigQuery, etc.) in partnership with data science team members to determine usability.
• Document data assets, how they are used, who owns/supports asset, and ETL processes (examples: data mapping, flow diagrams, data dictionary, business rules, quality checks, etc.).
• Create data publishing requirements to inform engineering partners.
• Validate and recommend new use cases for existing data.
• Collaborate with Product Owners/Managers and cross-functional development teams to ensure data needs and changes are articulated in Jira via Stories, Epics, Features.
• Partner with upstream data owners on data ingestion, changes, impacts, etc., and ensuring plans and communications are in place for adequate testing, quality, reliability, performance, etc.
• Establish and enforce data governance policies and procedures to ensure data integrity and compliance.
• Monitor data quality metrics and address issues to maintain high standards of data accuracy.

Preferred Qualifications

• Supply Chain data experience is a plus.
• Understanding of AI algorithms is a plus.