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Data Architect

Alignity Solutions


Job Location:

Hyderabad - India

Salary: Not provided by the employer
Experience Required: 12years
Posted: 25 September 2026 (Yesterday)
Application Deadline: 23 December 2026
Vacancies: 1 Vacancy

Job Summary

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Role:Data Scientist
Location: Hyderabad
Experience:12 Years
Work Mode: Hybrid
Type: Contract to Hire
Notice Period:Immediate Joiners


Requirements

Job Description Data Architect


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 platforms 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 vendors 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.

Required Skills:

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 scale. Proficiency in SQL and at least one programming language (Python preferred) sufficient to review and validate production data pipeline code against the intended model. Experience with relational databases (PostgreSQL preferred) for canonical model implementation at scale. Ability to distinguish role-level architecture responsibility from years of experience alone - evaluating what a candidate actually owned versus what they were exposed to or supervised. Hands-on proficiency with AI coding assistants (Claude Code GitHub Copilot Cursor Windsurf or equivalent) for architecture work design documentation and engineering workflows. Familiarity with agentic engineering patterns is expected. Strong communication and stakeholder management skills; ability to explain modeling trade-offs to both engineers and business/delivery stakeholders.