Sr. Analyst, Enterprise BI Data Products & Governance

Responsibilities

• Data Analysis, Modeling & Documentation
• Experience translating business and operational requirements into scalable data models and solution designs.
• Strong SQL, Python, and PySpark skills.
• Able to document requirements, define data relationships, and create detailed data lineage artifacts that support trusted enterprise analytics.
• Hands-on experience with AWS, MinIO/S3, Databricks/Spark, and modern data lake/lakehouse architecture principles.
• Experience managing and processing large-scale datasets.
• Strong understanding of data quality, governance, metadata management, security, and best practices for maintaining trusted enterprise data assets.
• Familiarity with field and network operations within enterprise support environments.
• Experience partnering with engineering teams to deliver scalable data solutions and proof-of-concept models that address operational and business needs.
• Managing data discovery, profiling, and documentation to support business unit reporting, analytics, and user queries.
• Serving as a subject matter expert, articulating data benefits and insights to business stakeholders.
• Collaborating with business units and data architecture teams to document enterprise data requirements.
• Estimating project efforts and timelines in collaboration with management and project teams.
• Maintaining comprehensive documentation throughout the data lifecycle for transparency and deadline adherence.
• Developing business rules and systems to harmonize and standardize data for organizational consistency.
• Leading the creation and execution of test plans for User Acceptance Testing of data-centric projects.
• Partnering with engineering to implement data governance and ensure the integrity of data solutions.

Qualifications

• Master's Degree in Data Science, Computer Science, Information Systems, or a related field.
• 5-7 years of relevant work experience in data analysis, cloud architectures, or enterprise data management.
• Proven experience in data modeling, SQL, Python, and PySpark.
• Hands-on experience with AWS, MinIO/S3, Databricks/Spark, and modern data lake/lakehouse architecture principles.
• Experience managing and processing large-scale datasets.
• Strong understanding of data quality, governance, metadata management, security, and best practices for maintaining trusted enterprise data assets.
• Familiarity with field and network operations within enterprise support environments.
• Experience partnering with engineering teams to deliver scalable data solutions and proof-of-concept models that address operational and business needs.
• Excellent communication and collaboration skills.
• Ability to manage multiple projects simultaneously and meet deadlines.
• Self-starter with the ability to work independently and take initiative.