Data Analyst
- Tanisha Systems Inc.
1 Days Ago
NA
Yearly
Austin-TX
7-10 Years
Required Skills: Python, SQL, AI for visualization, JIRA
Job Description
We are seeking a highly skilled Senior Data Analyst / Business Data Consultant to partner with our Data Team. This role is pivotal in bridging business strategy with technical execution — converting complex business needs into scalable, data-driven solutions. The successful candidate will lead detailed data analysis efforts, uncover business-critical insights, and identify data use cases that drive measurable impact across the organization. This project supports the company’s data modernization and analytics roadmap, enabling unified reporting, trusted master data, and scalable analytics across global
business units.
business units.
Key Responsibilities
• Data Analysis & Insight Generation: Perform in-depth analysis of enterprise data to uncover trends, patterns, and correlations. Identify actionable insights and data-driven opportunities that inform decision-making and strategic initiatives.
• Data Use Case Identification: Collaborate with stakeholders to identify and define data use cases across functional domains, ensuring alignment with
business priorities and future-state data strategy.
• Business-to-Technical Translation: Engage with stakeholders to translate business requirements into clear technical specifications, analytical models, and actionable deliverables.
• Impact Assessment: Analyze current-state data and infrastructure versus future-state goals; identify data and process gaps; define mitigation trategies.
• Data Profiling & Quality Analysis: Perform deep dives into structured and semi-structured datasets to assess completeness, consistency, and accuracy;
work with SMEs to understand data availability and source gaps.
• Data Integration & Transformation: Design and validate data pipelines and transformations; document lineage and dependencies.
• Feature Engineering & Analytics: Use Python and SQL to extract, clean, and engineer data features for analytics and modeling.
• AI-Assisted Analytics: Apply basic AI/ML and LLM-based tools (e.g., prompt- based data exploration, AI-assisted feature engineering, or generative AI
copilots) to accelerate data analysis, documentation, and insight generation.
• Reporting & Visualization: Build and maintain insightful dashboards and reports to communicate findings effectively.
• Governance & Documentation: Maintain business glossary, data dictionary, and metadata aligned with governance standards.
• Testing & Validation: Participate in validation of new or transformed data sets to ensure integrity and alignment with business requirements.
• Knowledge Management: Maintain the data dictionary and business glossary for assigned domains, capturing and documenting definitions aligned with
business leadership and global teams.
• Cross-functional Collaboration: Partner closely with data engineers, product managers, and business SMEs to align solutions with business objectives.
• Data Analysis & Insight Generation: Perform in-depth analysis of enterprise data to uncover trends, patterns, and correlations. Identify actionable insights and data-driven opportunities that inform decision-making and strategic initiatives.
• Data Use Case Identification: Collaborate with stakeholders to identify and define data use cases across functional domains, ensuring alignment with
business priorities and future-state data strategy.
• Business-to-Technical Translation: Engage with stakeholders to translate business requirements into clear technical specifications, analytical models, and actionable deliverables.
• Impact Assessment: Analyze current-state data and infrastructure versus future-state goals; identify data and process gaps; define mitigation trategies.
• Data Profiling & Quality Analysis: Perform deep dives into structured and semi-structured datasets to assess completeness, consistency, and accuracy;
work with SMEs to understand data availability and source gaps.
• Data Integration & Transformation: Design and validate data pipelines and transformations; document lineage and dependencies.
• Feature Engineering & Analytics: Use Python and SQL to extract, clean, and engineer data features for analytics and modeling.
• AI-Assisted Analytics: Apply basic AI/ML and LLM-based tools (e.g., prompt- based data exploration, AI-assisted feature engineering, or generative AI
copilots) to accelerate data analysis, documentation, and insight generation.
• Reporting & Visualization: Build and maintain insightful dashboards and reports to communicate findings effectively.
• Governance & Documentation: Maintain business glossary, data dictionary, and metadata aligned with governance standards.
• Testing & Validation: Participate in validation of new or transformed data sets to ensure integrity and alignment with business requirements.
• Knowledge Management: Maintain the data dictionary and business glossary for assigned domains, capturing and documenting definitions aligned with
business leadership and global teams.
• Cross-functional Collaboration: Partner closely with data engineers, product managers, and business SMEs to align solutions with business objectives.
Required Skills & Qualifications
• 8–12 years of experience in data analytics, business analysis, or data management.
• Advanced SQL expertise with ability to query and optimize large, complex datasets.
• Proficiency in Python for data analysis, transformation, and feature engineering (Pandas, NumPy).
• Experience with building dashboard using AI or similar visualization frameworks for data storytelling and dashboards.
• Deep understanding of data architecture, governance, MDM, and data lineage.
• Proven track record in identifying and scoping new data use cases and converting insights into business recommendations.
• Strong analytical thinking, stakeholder management, and communication skills.
• 8–12 years of experience in data analytics, business analysis, or data management.
• Advanced SQL expertise with ability to query and optimize large, complex datasets.
• Proficiency in Python for data analysis, transformation, and feature engineering (Pandas, NumPy).
• Experience with building dashboard using AI or similar visualization frameworks for data storytelling and dashboards.
• Deep understanding of data architecture, governance, MDM, and data lineage.
• Proven track record in identifying and scoping new data use cases and converting insights into business recommendations.
• Strong analytical thinking, stakeholder management, and communication skills.
• Basic working knowledge of AI/ML concepts and exposure to AI-assisted tools (e.g., Copilot-style coding assistants, LLM-based chat/analytics tools, or prompt engineering for data tasks) — no deep ML modeling expertise required.
• Experience with Snowflake, Databricks, or Hadoop/Spark environments.
• Exposure to data catalog or governance tools (Collibra, Alation).
• Familiarity with D&B data hierarchy, enrichment, or similar reference data systems.
Environment & Tooling Stack
• SQL (PostgreSQL, Snowflake, Redshift)
• Python (Pandas, NumPy, feature engineering)
• AI for visualization
• Git / Bitbucket for version control
• JIRA / Confluence for project tracking and documentation
• Experience with Snowflake, Databricks, or Hadoop/Spark environments.
• Exposure to data catalog or governance tools (Collibra, Alation).
• Familiarity with D&B data hierarchy, enrichment, or similar reference data systems.
Environment & Tooling Stack
• SQL (PostgreSQL, Snowflake, Redshift)
• Python (Pandas, NumPy, feature engineering)
• AI for visualization
• Git / Bitbucket for version control
• JIRA / Confluence for project tracking and documentation
