Lead Data Architect

lake/warehouse solutions (e.g., AWS Redshift/Snowflake/Databricks/BigQuery/Synapse), including performance, partitioning, and cost optimization. · Lead data integration and ETL/ELT architecture using tools like SSIS/Informatica/dbt/ADF/Glue/Airflow. · Own data modeling strategy: conceptual/logical/physical models, dimensional modeling, and canonical models for key domains. Governance, Quality, and Security · Define and enforce data governance: classification, retention, access controls, and stewardship models. · Establish data quality frameworks (rules, controls, monitoring), ensuring trusted datasets and consistent KPIs. ·Ensure compliance with security and regulatory requirements (PII/PHI/PCI/SOX/GDPR), including encryption, tokenization, and least privilege access. Stakeholder & Program Leadership · Partner with Product Owners, Engineering, Security, and business leaders to align architecture with strategic outcomes. · Lead architecture reviews, design approvals, and technical

decision forums; document ADRs (Architecture Decision Records). · Mentor data engineers/analysts; set engineering standards for code quality, CI/CD, and operational excellence. · Drive vendor evaluation and selection; support contracting, SOW reviews, and implementation oversight. Required Qualifications · 8-12+ years in data architecture / data engineering / enterprise data platform roles, with 2-5+ years leading architecture. · Strong experience designing cloud-scale data platforms (lake/warehouse/lakehouse) and data integration patterns. · Proven expertise in: o Data modeling (dimensional, normalized, semantic models) o ETL/ELT and pipeline orchestration o SQL and performance tuning o Data governance, lineage, and metadata management concepts · Strong understanding of modern data security patterns: IAM/RBAC/ABAC, encryption, key management, and audit logging. · Experience communicating architecture to both technical and non-technical stakeholders; ability to drive consensus. Preferred Qualifications (Nice to Have) · Experience with Amazon Redshift, Power BI, and enterprise semantic modeling (tabular models, star schemas, DAX optimization). · Exposure to data mesh, domain-based ownership, and data product operating models. · Experience with streaming/event platforms (Kafka/Kinesis) and real-time analytics patterns. · Familiarity with ML/AI enablement (feature stores, model monitoring, governance). · Certifications: AWS/Google Cloud Platform/Azure data certs, TOGAF (optional), DAMA/CDMP (optional). Key Deliverables · Enterprise data architecture target state and roadmap · Reference architectures, patterns, standards, and ADRs · Data platform designs (ingestion * processing * storage * serving) ·Data models and semantic layer strategy (BI-ready, governed KPIs) · Governance artifacts: classification, lineage, stewardship, access model · Performance/cost optimization recommendations and runbooks Success Metrics · Reduced time to deliver trusted datasets / analytics products · Improved data quality scores and reduction in recurring data defects ·Increased adoption of governed datasets and standardized KPIs · Platform performance and cost eAiciency (SLA attainment, optimized spend) · Audit readiness and reduction in compliance findings