What are the responsibilities and job description for the Information Architect – HR Analytics position at Technified Solutions Inc?
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Must Have Skills
Skill 1 – Enterprise Data Architecture (Conceptual, Logical & Physical Data Modeling)
Skill 2 – HR Analytics & HR Data Domains (Talent, Workforce, Compensation, Learning, People Analytics)
Skill 3 – Azure Data Platform (Databricks, Lakehouse, Medallion Architecture – Bronze/Silver/Gold)
Skill 4 – Microsoft Fabric (OneLake, Semantic Layer, Certified Data Products, Power BI Models)
Skill 5 – Data Integration Patterns (Batch, CDC, Streaming) & Integration Contracts
Skill 6 – Data Governance & Standards (Unity Catalog, Business Glossary Alignment, Data Quality)
Skill 7 – Hands-on Data Engineering (Complex Pipelines, Transformations, Performance Optimization)
Skill 8 – Technical Leadership (Architecture Reviews, Engineering Standards, DevOps Ownership)
Role Summary
The Information Architect is the end-to-end technical authority for the HR Analytics data estate, owning architecture from upstream authoritative source systems through to certified data products delivered in Microsoft Fabric. This role defines integration contracts with source-system owners, owns the conceptual, logical, and physical data models, aligns the enterprise business glossary, and establishes the engineering quality standards for all pipelines and data products.
This is a hands-on technical leadership role. The Information Architect designs systems, leads architecture and design reviews, contributes directly to complex pipeline implementation, and resolves the most challenging engineering problems across the platform. The role is delivery-adjacent, fast-paced, and directly shapes the quality of data products consumed by HR business stakeholders.
Scope of Responsibility
End-to-End Information Architecture Ownership
The Information Architect owns the full HR Analytics architecture across:
Upstream (Source Systems)
Own integration contracts with authoritative source systems including Workday, ServiceNow, internal HR platforms, and other systems of record.
Own the canonical data model and integration contract; source-system teams retain ownership of their internal implementations.
Midstream (Data Platform & Lakehouse)
Define ingestion patterns and medallion architecture (bronze, silver, gold).
Own transformation logic, Databricks lakehouse design, Unity Catalog governance, and Azure data platform standards.
Downstream (Consumption & Analytics)
Own semantic layer design and certified data product delivery in Microsoft Fabric.
Define Fabric IQ patterns, OneLake organization, and consumption-ready models for Power BI and AI/ML workloads.
How the Team Works
The HR Analytics engineering team operates as one global team across the US and India.
The team is structured into three layers:
Information Architect – owns end-to-end architecture and engineering standards.
Principal Data Engineers – set the engineering bar and lead delivery.
Senior, Data, and Associate Data Engineers – deliver data products end-to-end.
Engineers are distributed across Frisco, TX; PST US locations; and Hyderabad, India, partnering across time zones to maintain ownership and delivery momentum.
Business engagement is led through senior engineering and architecture leadership, with broader team participation growing as engineers gain domain expertise.
Work flows from business need → technical design → ingestion, unified framework, data modeling, transformation across Databricks and Snowflake, semantic layer in Microsoft Fabric, and visualization in Power BI.
The team operates a DevOps ownership model, including shared on-call rotation and production support.
Core Responsibilities
Own the end-to-end information architecture for HR Analytics, from authoritative source systems through ingestion, transformation, lakehouse, semantic layer, and certified Fabric data products.
Own and maintain conceptual, logical, and physical data models for HR Analytics, aligned to the enterprise business glossary and HR domain semantics across:
Talent
Workforce
Compensation
Learning
People Analytics
Define and own integration contracts with upstream source-system teams, including:
Data delivery patterns (batch, CDC, streaming)
Schema agreements
SLA commitments
Change management protocols