Data Architect

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Role:Data Architect
Location: Hyderabad,Pune,Gurgaon,Chennai,Bangalore,Kolkata,Mumbai.
Experience:12+ YearsWork Mode: Hybrid
Type: Contract to Hire
Notice Period:Immediate Joiners

Requirements

Job Description – Data Architect

Key Skills: SQL, Data Modeling, data warehouse knowledge & Cloude platform, azure, GCP AWS, CICD, Phyton

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 hardcoding) 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: 8-12 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.