- Job ID: 11084BR
- Location: India (Remote)
- Work Setup: Remote
- Job Category: Finance
- Posting Date: June 29, 2026

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 processes—including canonical schemas, API contracts, and ETL/ELT pipeline specifications—that support both real-time operational workflows and batch analytics consumption.
- Maintain a single trusted view of master data—including golden record definitions, match-merge rules, and certified entity attributes—to support reliable decision-making and business growth.
- Diagnose data-related issues—including referential integrity failures, schema drift, duplicate entity proliferation, and null/inconsistency patterns—and 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 transparent,



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 domains—validated 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.

Required Qualifications

- Data Modeling: Expert command of logical and physical data modeling, including dimensional modeling (star/snowflake schemas), Data Vault, and canonical model design—with 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 issues—including referential integrity failures, schema drift, duplicate proliferation, and null patterns—and recommend targeted solutions.
- Graph-Based Lineage:



Exposure to graph-based lineage tools for tracing field-level data flow from source systems through to model outputs.
- Cloud Platforms: Working knowledge of Snowflake or Google BigQuery and their native capabilities for metadata management, data sharing, and schema governance.
- Documentation: Ability to author clear data requirement specifications, field-level mapping documents, data dictionaries, and lineage documentation for both technical and non-technical audiences.
- Experience: 5+ years in data engineering, data modeling, or master data management roles within enterprise data environments.
- Education: Bachelor’s degree in computer science, Information Systems, Data Engineering, or a related technical field.

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📌 Senior Data Analyst (India)
🏢 Deltek
📍 India