Data Modeler Senior

Short Description for Candidates

The Senior Data Analyst interprets complex data sets and transforms them into actionable insights that enhance business decision-making and improve overall data usage within the organization. The role involves gathering information from multiple sources, identifying trends and patterns, ensuring data quality, and developing solutions that support business objectives. This position is also responsible for championing continuous improvement in data processes, coaching junior team members, and staying current with emerging data science and AI/ML trends.

*Description for Candidates

The Senior Data Analyst interprets complex data sets and transforms them into actionable insights that enhance business decision-making and improve overall data usage within the organization. The role involves gathering information from multiple sources, identifying trends and patterns, ensuring data quality, and developing solutions that support business objectives. This position is also responsible for championing continuous improvement in data processes, coaching junior team members, and staying current with emerging data science and AI/ML trends.

Key Responsibilities

• Conduct descriptive and diagnostic analytics on diverse data sources; present insights and recommendations to senior leadership.
• Apply advanced machine learning and data science tools to improve data quality and identify key patterns and trends.
• Create, manage, and optimize complex reports, dashboards, and data visualizations using BI tools and technologies.
• Develop and maintain data profiles for tables and elements within data lakes; ensure accuracy and completeness in data catalogs.
• Analyze complex, multi-source data sets to identify data quality, integration, and redundancy issues, and propose effective solutions.
• Design and develop for Big Data platforms leveraging open-source and third-party tools.
• Collaborate with stakeholders to implement data cleansing methods, governance solutions, and metadata management practices.
• Document key decisions, rules, processes, and provide training material to ensure knowledge continuity.
• Stay updated on AI/ML and data technology trends; recommend and integrate innovative tools and methods into existing workflows.
• Mentor and coach less experienced team members, providing technical and analytical guidance.

*Qualifications and Competencies

Competencies

• Data Literacy: Translate data into business context, applying analytical methods effectively.
• Data Quality & Governance: Identify and resolve data flaws while ensuring strong governance practices.
• Visualization & Communication: Build clear, impactful dashboards and reports, tailoring communication to diverse stakeholders.
• Analytical Thinking: Apply statistical, quantitative, and problem-solving methods to uncover insights.
• Project Management: Balance scope, schedule, and resources for data initiatives.
• Tech Savvy & Innovation: Stay ahead of emerging tools, trends, and technologies.
• Customer Focus: Build strong relationships and deliver customer-centric solutions.
• Stakeholder Management: Balance and anticipate the needs of multiple stakeholders effectively.
• Leadership & Mentorship: Guide, coach, and support team members while fostering collaboration.

Skills and Experience

Core Technical Skills:

• Data Modeling: Strong experience in Dimensional Modeling, 3NF, and schema design.
• ETL/ELT Tools: Hands-on with data engineering technologies and pipelines.
• Big Data & Cloud: Exposure to Big Data open-source tools and cloud-based clustered compute implementations.
• Data Profiling & Cataloging: Proficiency with data profiling tools, catalog tools (e.g., Azure Purview, Alation), and metadata management.
• Business Intelligence: Expertise with BI tools and technologies for reporting and dashboards.
• Platforms: Experience with Snowflake, Databricks, and other modern cloud data platforms.
• AI/ML: Understanding of AI/ML concepts, frameworks, and their application in data quality and process improvements.
• Programming: Proficiency in SQL coding, with exposure to open-source technologies for Big Data.

Preferred / Nice-to-Have:

• Experience with graph data modeling and graph databases (Neo4j, TigerGraph).
• Familiarity with Palantir Ontology.
• Exposure to IoT technologies and related data challenges.
• Strong technical writing and documentation skills.

Required Qualifications

• Bachelor’s or equivalent degree in Computer Science, Data Science, Information Systems, or a related technical discipline (or equivalent practical experience).
• 5–8 years of hands-on experience in data analysis, data modeling, data engineering, or a related field.
• Proven expertise in designing and implementing scalable, optimized data models and architectures.
• Solid experience in SQL and NoSQL querying across multiple platforms.
• Exposure to Agile software development practices.

Work Timings - 12 PM to 9 PM