Data Scientist

Position: Data Scientist :: 5-8 Years & 8+ Years :: Multiple openings
Data Scientist
Total positions:2
Positions Level
Sr. Consultant

Location:

USI Location (Hyderabad, Bangalore, Pune, Mumbai, Kolkata, Chennai, Gurgaon)

Experience:

3-8 + Years
Shift Time: 11 am to 8 pm
Work Mode:
Hybrid

Mandatory

Skills:

Pyspark, Machine learning, Azure, ML Models, Azure, synapse, Data Bricker’s. phyton-Scripting, Lag chain

Role Summary
We are seeking a Data Architect to own canonical data modeling and abstraction discipline across our platforms, most notably our payroll-system-agnostic platform built on a Common Data Model (CDM) that normalizes multiple vendor payroll systems into one canonical structure. This role is distinct from a Data Integration Architect: rather than owning pipeline mechanics (ETL/ELT, lakehouse, vector stores), you own the correctness of the model itself - how source-system concepts get abstracted into stable canonical entities, how configuration (not hard coding) drives extensibility as new sources and clients are onboarded, and how the platform's engineering stays reconciled against ground-truth data rather than assumptions.

You will work closely with data engineering, application engineering, and delivery leadership to ensure the data model can scale to new payroll vendors and clients without being redesigned each time.

Experience bands:
Manager: 12+ years total experience in data architecture, data modeling, or related engineering roles, including experience owning a canonical/common data model used across multiple clients or source systems

Key Responsibilities
Own canonical data modeling: define stable, source-agnostic entities and relationships that represent the business domain (e.g., payroll constructs such as Gross Pay, Net Pay, Regular Pay, Bonus, Termination) independent of any single vendor's data shape.
Design config-driven architecture: ensure new source systems, clients, or field variations are onboarded through configuration and mapping rules rather than one-off code changes or schema forks.
Enforce correctness and reconciliation discipline: define and validate that canonical data reconciles against source system totals and known ground truth, and build in checks that surface drift or silent data-quality regressions early.
Own production engineering and abstraction discipline: review and approve how abstraction layers are actually implemented in code, ensuring the conceptual model and the production implementation do not diverge over time.
Partner with the Solution Architect and Data Integration Architect: this role owns the correctness and stability of the canonical model itself, while those roles own end-to-end platform coherence and pipeline/ingestion mechanics respectively.
Lead data modeling reviews and design walkthroughs for new entities, new client onboarding, or new source-system integrations, pressure-testing proposed models against edge cases and real production data.
Define data governance practices for the canonical layer: versioning of the model, change management for schema evolution, lineage, and documentation standards.
Validate architecture and modeling decisions directly against real client data (not sampled or synthetic data) before sign-off, and flag contradictions between assumed and actual data behavior.
Create architecture artifacts (canonical model documentation, ADRs, entity-relationship diagrams, mapping specifications) and maintain them as the model evolves.
Mentor data engineers on modeling discipline and abstraction practices; review code and configuration for adherence to the canonical model rather than workarounds.
Support onboarding of new foundation clients by assessing how their source systems map to the existing canonical model and where the model needs to be extended versus where a client-specific exception is being incorrectly introduced.

Required Qualifications
7+ years (Senior Consultant) or 11+ years (Manager) total experience in data architecture or data modeling roles, including demonstrated ownership of a canonical or common data model spanning multiple source systems or clients.
Strong hands-on experience with dimensional and canonical data modeling, including designing entities that stay stable while underlying source systems vary or change.
Demonstrated experience designing config-driven (not hardcoded) architecture for onboarding new data sources, clients, or variations without redesigning the core model.
Strong track record of building or enforcing reconciliation and data-quality validation practices against ground-truth data at production…