Senior Data Analyst

• Collaborate with business stakeholders to understand data needs and translate them into analytical and reporting requirements.
• Design, create, and implement new data quality rules aligned with business and regulatory requirements.
• Ensure data accuracy, consistency, and integrity through systematic validation, profiling, and quality checks.
• Analyze source systems and datasets to identify patterns, anomalies, and data inconsistencies.
• Build, optimize, and maintain SQL queries, views, and data models to support reporting and analytics.
• Demonstrate strong proficiency in PL/SQL, including development of stored procedures and performance optimization.
• Contribute to the definition and standardization of KPIs, metrics, and enterprise data governance standards.
• Investigate data quality issues, perform root‑cause analysis, and recommend solutions for data standardization and remediation.
• Develop and maintain data dictionaries and metadata documentation for Critical Data Elements (CDEs).
• Respond to and resolve inquiries from the Data Stewardship mailbox, ensuring timely and accurate support.

Additional Responsibilities (AI / ML / LLM Focus)

• Apply ML techniques for anomaly detection, classification, and predictive analytics.
• Use Python for data analysis and automation
• Support integration of AI/ML models into data pipelines and reporting
• Leverage LLMs for data summarization, metadata enrichment, and natural language querying
• Implement AI-driven data quality monitoring and intelligent rule generation
• Contribute to AI-enabled analytics solutions for enhanced insights

• Education: Bachelor’s/master’s degree in engineering/computer applications
• Strong understanding of RDMBS databases such as Oracle and SQL Server
• Good understanding of Business Intelligence tools such as MicroStrategy and Power BI
• Expert proficiency in SQL for data extraction, manipulation, and analysis.
• Solid understanding of database structures, data warehousing concepts, and data governance principles.
• Proven ability to work independently, manage multiple projects, and prioritize effectively.
• Exceptional analytical and problem-solving skills, with the ability to tackle complex business challenges and provide innovative solutions.
• Hands-on experience with Python for data analysis and ML. Knowledge of ML algorithms (regression, classification, clustering)
• Familiarity with LLMs (prompt engineering, embeddings, GenAI use cases)
• Understanding of AI/ML lifecycle (data prep, modeling, evaluation, deployment)

Mid Shift (2PM – 11PM), No Travel required.