Data Architect

Essential Duties and Responsibilities include the following but are not limited to the job specifications contained herein.  Additional duties or job functions that can be performed safely may be required as deemed necessary by supervisory personnel.

• Own the architecture of the data platform end-to-end: warehouse design, medallion layer conventions (bronze, silver, gold), transformation standards, orchestration patterns, and semantic layer governance.

• Define what each medallion layer means in practice: what lands in bronze as raw source data, what gets cleaned and conformed in silver, what gets shaped into business-ready models in gold, and hold those boundaries consistently as the platform evolves.

• Own platform health: track and manage compute costs, query performance, pipeline reliability, and model freshness. Identify degradation before it reaches business users.

• Set ingestion standards for new data sources: landing zone design, schema evolution handling, change detection patterns, and failure recovery approaches.

• Serve as the technical escalation point for the India Operations engineering team. Conduct architecture reviews, provide design guidance, and review code for pattern compliance.

• Maintain platform documentation: architecture decision records, data flow diagrams, runbooks, and post-incident reviews.

• Evaluate new tools and approaches and own the technical roadmap for platform infrastructure.

• Partner with Data Governance on quality enforcement, lineage tracking, and access control design across the warehouse and downstream tools.

• Define and maintain the semantic layer, a single authoritative definition of business metrics that any report or analyst can rely on.

• Partner with analytics and BI teams to validate the semantic layer reflects how the business asks questions and define row-level security and access patterns that control metric exposure by role.

• Get involved early in cross-functional projects to help shape data requirements and platform needs before implementation begins, rather than inheriting decisions after the fact.

• Sit on the Technical Review Board, evaluating major architectural decisions and engineering practices for consistency with platform standards before they are implemented.

• Define and maintain enterprise data standards - naming conventions, documentation requirements, and data quality benchmarks - that apply consistently across the platform.

• Own the role-based access control (RBAC) design across the warehouse, defining role hierarchies, privilege tiers, and access patterns that balance self-serve analytics with data security requirements. This includes functional roles, object-level grants, and a governance process for access requests.

• Design and maintain a partitioned virtual warehouse strategy, right-sizing compute clusters for distinct workload types (pipeline execution, ad-hoc analytics, BI tool queries) to control cost and prevent resource contention between workloads.

• Own the environment architecture: dev, test, and production hierarchy, including promotion workflows and the guardrails that prevent lower-environment changes from reaching production without review.

• Define storage optimization patterns: clustering keys, table design decisions, and partition strategies that keep query performance predictable as data volumes grow.

• Govern data masking policies, dynamic data access controls, and network-level security configurations to meet internal security standards and external compliance requirements.