Senior Data Analyst - SQL

Senior Data Analyst Data Quality &

• AI Enablement

Experience : 10 Years

Location : Bangalore (Bagmane Solarium)

Mode of Work : Hybrid (3 Days Mandatory from Office)

Key Responsibilities :

Data Quality Management &

• Validation :
• Perform comprehensive data profiling, quality assessments, and health checks across Customer, Sales, CRM, and Master Data domains.
• Design, execute, and monitor data quality rules to identify anomalies, inconsistencies, duplicates, missing values, and integrity issues.
• Conduct root cause analysis for data quality issues and collaborate with data owners and technical teams to implement corrective actions.
• Define, maintain, and enhance data quality frameworks, standards, and monitoring processes.
• Track, measure, and report data quality KPIs and metrics through dashboards and executive reports.
• Ensure data consistency across multiple enterprise applications and systems.

AI Enablement &

• Ground Truth Validation :
• Partner with Business SMEs and AI/ML teams to gather, validate, and maintain high-quality ground truth datasets.
• Support data labeling, annotation, classification, and validation activities for AI model development and training.
• Validate input and output datasets to ensure alignment with business requirements and expected outcomes.
• Perform feature validation and data readiness assessments for AI and Machine Learning initiatives.
• Conduct pre-UAT reviews to ensure data quality and model readiness before deployment.
• Assist in model evaluation by validating predictions against business-defined acceptance criteria.

Business Requirements Analysis :

• Gather, analyze, and document business, data, and reporting requirements for AI and analytics initiatives.
• Translate business requirements into detailed functional specifications and data requirements.
• Define transformation rules, mapping documents, business rules, and acceptance criteria.
• Develop and maintain data flow diagrams across Customer Master, CRM, Sales, and downstream systems.
• Conduct impact assessments for changes affecting data quality, governance, and reporting.

Data Governance &

• Compliance :
• Ensure adherence to enterprise data governance standards, policies, and best practices.
• Maintain data dictionaries, business glossaries, metadata repositories, and lineage documentation.
• Support compliance requirements related to personal, confidential, and sensitive data handling.
• Implement data minimization principles and ensure proper usage of customer and sales data.
• Identify potential data risks and maintain risk registers, mitigation plans, and issue logs.
• Collaborate with governance teams to improve data stewardship and accountability.

UAT, Testing &

• Deployment Support :
• Design and execute test cases focused on data validation, business rules, and AI model outputs.
• Conduct User Acceptance Testing (UAT) and coordinate defect tracking, prioritization, and resolution.
• Validate system outputs against business logic, transformation rules, and acceptance criteria.
• Ensure deployment readiness through comprehensive testing and validation activities.
• Support production rollout activities and post-deployment verification.
• Prepare handover documents, knowledge transfer materials, and operational support artifacts.

Project &

• Stakeholder Management :
• Manage end-to-end data quality initiatives, including planning, execution, monitoring, and sign-off.
• Collaborate with business users, product owners, architects, engineers, data scientists, and governance teams.
• Facilitate workshops, requirement-gathering sessions, and data review meetings.
• Provide regular project status updates, risk assessments, and executive-level reporting.
• Support Agile delivery practices, including sprint planning, backlog grooming, and retrospectives.

Required Skills &

Experience :

Technical Skills :

• 10 years of experience in Data Analysis, Data Quality Management, Data Governance, or Data Management projects.
• Strong expertise in data profiling, data cleansing, reconciliation, validation, and quality monitoring.
• Experience working with large-scale datasets and Big Data platforms.
• Advanced SQL skills for data extraction, validation, and analysis.
• Hands-on experience with data quality tools and data governance frameworks.
• Strong understanding of Customer Data, Sales Data, CRM systems, and Master Data Management (MDM).
• Experience with data lineage, metadata management, and data cataloging concepts.
• Knowledge of AI/ML data preparation, ground truth creation, and validation processes.
• Familiarity with cloud-based data platforms and modern analytics ecosystems.

Project Management &

• Delivery :
• Proven experience managing Data Quality and Data Governance initiatives from planning through sign-off.
• Strong understanding of Agile and Scrum methodologies.
• Experience in defect management, risk tracking, issue resolution, and release management.
• Ability to prioritize multiple deliverables and manage competing business requirements.

Soft Skills :

• Excellent stakeholder management and communication skills.
• Strong analytical and problem-solving abilities.
• Ability to influence cross-functional teams and drive data quality improvements.
• Exceptional documentation and presentation skills.
• Strong attention to detail and commitment to data accuracy.

Preferred Qualifications :

• Experience supporting AI, Machine Learning, or Generative AI initiatives.
• Knowledge of Customer Master Data Management (MDM) frameworks.
• Exposure to enterprise CRM platforms such as Salesforce, SAP, Microsoft Dynamics, or similar systems.
• Experience with data governance and quality tools.
• Relevant certifications in Data Management, Data Governance, Agile, or Project Management will be an added advantage.

Success Criteria :

• Improved data quality scores across Customer and Sales domains.
• Accurate and validated datasets supporting AI initiatives.