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 • Bachelor’s or Master’s 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).