Data Architect

Senior Data Architect (Enterprise Data Platforms)

Role Summary

The Senior Data Architect is responsible for defining and delivering enterprise-grade data architectures and reference implementations across complex, regulated environments. You will design scalable, secure, and cost-e@icient data platforms and products spanning batch and real-time processing, lakehouse/warehouse, governance, lineage, and master/reference data. You will partner with Engineering, Security, Infrastructure, BI, and Data Science teams to translate business needs into target-state architectures, standards, and execution roadmaps. Key Responsibilities Architecture & Strategy Define target-state enterprise data architecture and multi-year modernization roadmaps (cloud and hybrid). Select and standardize architecture patterns for ingestion, transformation, storage, serving, and observability (batch, streaming, event-driven). Establish architecture principles and reusable reference architectures for lakehouse, data warehouse, and data mesh/federated models. Drive technical decision-making across build vs. buy, platform/tool selection, and TCO optimization. Solution Design & Delivery Enablement Produce high-quality architecture artifacts: logical/physical models, integration patterns, data flows, domain/data product designs, and non-functional requirements. Provide technical leadership through delivery phases (inception design implementation deployment operations), partnering closely with engineering leads. Define reusable frameworks, templates, and accelerators (e.g., standardized pipeline patterns, data quality checks, governance integration). Governance, Quality, Security & Compliance Define and enforce enterprise standards for: o Data modeling (conceptual/logical/physical), naming conventions, and domain boundaries o Data quality, validation, and SLAs/SLOs o Metadata management, cataloging, and lineage o Privacy, security, access controls, encryption, and retention Architect and guide implementation of governance platforms (catalog, lineage, stewardship workflows) and ensure alignment with regulations (GDPR, CCPA, HIPAA as applicable). Master & Reference Data (MDM) Architect MDM and reference data solutions including domain ownership, golden record strategies, survivorship rules, and integration patterns. Define how master/reference data is published/consumed across analytical and operational systems. Stakeholder Partnership & Technical Leadership Act as a primary technical advisor for stakeholders, translating business objectives into pragmatic architectural plans. Lead architecture reviews, mentor senior engineers, and promote best practices across teams. Partner with Product, BI/Analytics, Security, and Platform teams to ensure coherent enterprise-wide solutions. Required Skills & Experience 10+ years in data engineering/architecture, with significant experience designing enterprise-scale data platforms. Deep expertise in data platform architectures and patterns: o Batch + real-time streaming (e.g., Spark, Kafka) o Warehousing and lakehouse (e.g., Snowflake, Databricks/Delta Lake, BigQuery, Redshift, Synapse) o Data mesh/data product thinking and federated governance concepts Strong cloud architecture experience in at least one major provider (AWS, Azure, or GCP), including networking and security fundamentals as they relate to data platforms. Advanced SQL and data modeling expertise; strong understanding of partitioning, performance tuning, and cost optimization. Strong knowledge of storage formats and table technologies (Parquet/ORC; Delta/Iceberg/Hudi). Hands-on experience designing ETL/ELT pipelines and orchestration (e.g., Airflow, Dataflow, Glue, NiFi). Proven experience with metadata, cataloging, and lineage solutions (e.g., AWS Glue Data Catalog, Azure Purview/Microsoft Purview or equivalents). Strong understanding of data privacy, security controls, and compliance-bydesign for enterprise data systems. Excellent communication skills with the ability to present architecture decisions to both technical teams and executive stakeholders. Preferred Qualifications Bachelors or Masters degree in Computer Science, Data Engineering, or related field (or equivalent practical experience). Relevant certifications (preferred): o AWS (Data Analytics / Solutions Architect), Google Professional Data Engineer, Azure Data Engineer Associate, Databricks/Snowflake certifications Experience with hybrid and multi-cloud deployments and large-scale migration/modernization programs. Experience with data observability and quality frameworks (e.g., Great Expectations, Deequ) and end-to-end monitoring practices. Familiarity with CI/CD and DataOps practices for data platforms; knowledge of MLOps integration patterns is a plus. Experience with BI/semantic layer design and enablement (Power BI, Tableau, Looker, etc.) for complex enterprise reporting. Additional / Desirable Experience Hands-on contributions to open-source data ecosystem tools (dbt, Delta, Iceberg, Great Expectations, etc.). Experience establishing enterprise metadata strategy and operating model (stewardship, ownership, domain governance). Ability to design and evangelize data democratization capabilities (self-service discovery, governed access, standard data products).