Business Data Analyst/ Architect

” 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 strategies. “ 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.

” 10 years of experience in data analytics, business analysis, or data management. “ Advanced SQL expertise with ability to query and Eligible to workimize 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