Data Architect Leader

Role Summary The Data Architect Leader is accountable for leading enterprise client engagements to build, modernize, and operationalize large-scale data platforms and data products in complex, regulated environments. This role combines hands-on architecture leadership with program-level guidance, shaping client strategy, target-state architectures, governance operating models, and multi-year transformation roadmaps across cloud, hybrid, and multi-cloud footprints. You will serve as a senior trusted advisor to executives and delivery leaders—driving architecture decisions, guiding multiple workstreams/teams, establishing standards and reference implementations, and ensuring solutions are secure, scalable, cost-efficient, and measurable in outcomes, including enablement of GenAI and agentic capabilities on governed enterprise data.

Key Responsibilities 1) Client & Engagement Leadership Lead end-to-end architecture for enterprise modernization programs (discovery → strategy → delivery → run), aligning business outcomes, technology decisions, and delivery plans. Serve as executive-facing advisor, facilitating trade-off decisions across scope, risk, cost, timeline, and operating model. Lead architecture governance within engagements: architecture review boards, decision logs, standards enforcement, and exception management. Shape “platform + product” delivery models that align data platform modernization with analytics and

AI / Agentic product

outcomes (time-to-value, adoption, risk posture). 2) Enterprise Data Platform Strategy & Roadmaps Define target-state enterprise data architecture and multi-year transformation roadmaps (platform, operating model, governance, migration waves). Establish and standardize architecture patterns for: Ingestion (batch/streaming/event-driven) Transformation (ELT/ETL, distributed processing) Storage (lakehouse/warehouse, domain-oriented stores) Serving (semantic layers, APIs, reverse ETL, activation) Observability (quality, lineage, reliability, FinOps) Define platform strategy that supports

AI-ready data

(data contracts, metadata completeness, lineage, quality SLOs, and curated “golden” datasets for AI use cases). 3) Solution Architecture & Reference Implementations Produce high-quality artifacts and lead their adoption across teams: Enterprise logical/physical data architecture Domain/data product designs and contracts Integration patterns (including ERP systems), data flows, and NFRs (availability, RPO/RTO, performance, cost) Security architecture for data (identity, network boundaries, key management, encryption, secrets) Define reusable reference architectures and accelerators (templates for pipelines, IaC patterns, CI/CD for data, quality checks, governance integration). 4) Governance, Security, Privacy & Compliance-by-Design Define and enforce enterprise standards for: Data modeling (conceptual/logical/physical), naming, domain boundaries, and data contracts Data quality rules, SLA/SLO definitions, and issue management workflows Metadata, cataloging, lineage, stewardship workflows, and auditability Privacy/security controls: RBAC/ABAC, encryption, retention, masking/tokenization, consent (as applicable) Architect governance platforms and operating models aligned to regulatory requirements (GDPR, CCPA, HIPAA as applicable), including controls evidence and audit readiness. 5) Master & Reference Data Leadership (MDM) Lead design of MDM/reference data strategies: domain ownership, golden record patterns, survivorship rules, match/merge approaches, and stewardship workflows. Define publication/consumption patterns for master/reference data across analytical and operational ecosystems. 6) GenAI & Agentic Solution Architecture Lead architecture and solutioning for

GenAI and agentic use cases

on enterprise data (e.g., RAG, semantic search, agent-assisted analytics, automated data operations, customer/employee copilots). Establishing scalable patterns for GenAI on governed data. Define end-to-end

LLMOps/AgentOps

practices: prompt/version management, evaluation harnesses, offline/online testing, monitoring, cost controls, rollback strategies, and production gating. Architect agentic workflows using agent SDKs/frameworks (e.g.,

Google ADK ,

AutoGen ) including: Tool/function calling patterns, planning vs. execution separation, memory strategies, and human-in-the-loop controls Guardrails (policy checks, groundedness, citations/attribution patterns, refusal behavior), and safe tool access Guide platform adoption and reference implementations leveraging: Databricks AI capabilities

(e.g.,

Mosaic AI , model serving, governed feature/embedding pipelines, agent/assistant patterns,

Agent Bricks / packaged agent accelerators

where relevant to the client ecosystem) Snowflake AI capabilities

(e.g.,

Cortex

and native AI/ML services for enterprise workloads) Design

knowledge graph / semantic layer strategies

to improve retrieval quality and reasoning (entity resolution, ontologies/taxonomies, relationships, graph + vector hybrid patterns, and governance of business definitions). Strong understanding of LLM/GenAI risks and controls: PII handling, prompt injection risks, data exfiltration controls, safe retrieval patterns, and evaluation/monitoring. 7) People Leadership, Mentorship & Practice Building (if applicable) Mentor and coach architects and senior engineers; establish architecture career paths, skill standards, and review processes. Contribute to practice capabilities: reusable assets, playbooks, reference architectures, and internal enablement (brown bags, training). Support pre-sales and solutioning: discovery workshops, proposals/SOW input, architecture in RFP responses, and delivery approach definition. Build repeatable offerings (platform modernization + GenAI enablement packages) with clear scope, outcomes, and implementation patterns. Required Skills & Experience 15+ years in data engineering/architecture with significant enterprise-scale platform design and modernization experience. Demonstrated leadership of multi-team, multi-workstream data programs (including distributed/onshore-offshore and/or multi-vendor environments). Deep expertise in modern data architectures and patterns: Batch + real-time streaming/event-driven (e.g., Spark, Kafka) Warehousing and lakehouse (e.g., Snowflake, Databricks/Delta Lake, BigQuery, Redshift, Synapse) Data product and federated/data mesh-aligned thinking (with pragmatic governance) Strong cloud architecture depth in at least one major provider (AWS/Azure/GCP), including networking, identity, and security patterns for data platforms. Strong knowledge of storage formats and table technologies (Parquet/ORC; Delta/Iceberg/Hudi). Proven ability to implement or guide ETL/ELT and orchestration patterns (Airflow, dbt, Dataflow, Glue, ADF, NiFi, etc.). Hands-on knowledge of metadata/catalog/lineage solutions (e.g., AWS Glue Data Catalog, Microsoft Purview, Databricks Unity Catalog or equivalents). Executive-level communication skills: can lead architecture trade-offs clearly to technical teams and business leadership.

Preferred Qualifications BS/MS in Computer Science, Data Engineering, or related field (or equivalent practical experience). Certifications (preferred): AWS Solutions Architect / Data Analytics, Google Professional Data Engineer, Azure Data Engineer, Databricks/Snowflake. Experience with hybrid/multi-cloud and large migration programs (warehouse-to-lakehouse, on-prem to cloud, Hadoop modernization, etc.). Data observability and quality frameworks (Great Expectations, Deequ) plus end-to-end monitoring/SLO practices. Strong CI/CD + DataOps patterns (IaC, environment promotion, testing, policy-as-code); MLOps integration patterns a plus. Semantic layer / BI enablement experience (Power BI, Tableau, Looker) for governed enterprise reporting. Hands-on experience with one or more AI suites/platforms:

Gemini Enterprise ,

Databricks Mosaic AI ,

Snowflake Cortex

(or equivalent).