Sr Data Strategist

29-Jun-2026 Senior Data Analyst India (Remote) 11084BR Company Summary As the recognized global standard for project-based businesses, Deltek delivers software and information solutions to help organizations achieve their purpose. Our market leadership stems from the work of our diverse employees who are united by a passion for learning, growing and making a difference. At Deltek, we take immense pride in creating a balanced, values-driven environment, where every employee feels included and empowered to do their best work. Our employees put our core values into action daily, creating a one-of-a-kind culture that has been recognized globally. Thanks to our incredible team, Deltek has been named one of America's Best Midsize Employers by Forbes, a Best Place to Work by Glassdoor, a Top Workplace by The Washington Post and a Best Place to Work in Asia by World HRD Congress. Position Responsibilities Key Responsibilities Develop and maintain logical and physical data models, database designs, and master data standards that ensure data is structured for efficient use across operational and analytical consumers. Build and maintain a unified, authoritative source of truth for master data entities by designing governed hub structures, defining survivorship rules, and resolving cross-system discrepancies to eliminate data silos. Operationalize critical data elements (CDEs) and product taxonomy by defining attribute-level rules, classification hierarchies, and standardized value sets that ensure consistent field usage across models, pipelines, and reporting layers. Design and maintain master data processesincluding canonical schemas, API contracts, and ETL/ELT pipeline specificationsthat support both real-time operational workflows and batch analytics consumption. Maintain a single trusted view of master dataincluding golden record definitions, match-merge rules, and certified entity attributesto support reliable decision-making and business growth. Diagnose data-related issuesincluding referential integrity failures, schema drift, duplicate entity proliferation, and null/inconsistency patternsand recommend targeted solutions to improve data integrity, security, and usability. Maintain lineage documentation connecting raw source fields to governed definitions and downstream model consumption; version-control data dictionaries and field-level mapping documents. Support data migration and system transition initiatives by assessing legacy data structures, identifying quality gaps, and mapping source fields to target canonical models. Work closely with data engineering, data architecture, analytics, and product teams to align master data standards, embed requirements early in the development cycle, and deliver consistent data outputs across all team boundaries. Run focused working sessions with EDI leads and subject-matter experts to gather data requirements, validate field definitions, and resolve conflicting interpretations across systems. Share clear, concise updates on data model progress, field classification status, and domain coverage with both technical teammates and non-technical stakeholders to maintain shared understanding and trust in data assets. Success Metrics Unified source of truth established for master data domainsvalidated through reduction in duplicate entity records, survivorship rule coverage, and cross-system discrepancy rate. CDE and product taxonomy operationalization: critical data elements and product taxonomy classifications defined, documented, and embedded in at least one active data model or pipeline within the first two quarters. Model enablement velocity: data model requirements documentation delivered on time to unblock 1 downstream model update per quarter, with no rework cycles caused by incomplete or inaccurate field definitions. Cross-team effectiveness: positive feedback from data engineering, analytics, and product teammates on the clarity of data requirements, responsiveness in working sessions, and usefulness of delivered documentation. Qualifications Required Qualifications Data Modeling: Expert command of logical and physical data modeling, including dimensional modeling (star/snowflake schemas), Data Vault, and canonical model designwith hands-on experience building and maintaining models in enterprise data environments. SQL Proficiency: Expert-level SQL for interrogating complex multi-schema environments, profiling data quality, and validating model outputs. Integration & Pipeline Design: Working knowledge of ETL/ELT pipeline design, API contracts, and canonical schema patterns that bridge operational workflows and analytics consumption. Data Diagnostics: Ability to identify and resolve data integrity issuesincluding referential integrity failures, schema drift, duplicate proliferation, and null patternsand recommend targeted solutions. Graph-Based Lineage: Exposure to graph-based lineage tools for tracing field-level data flow