Data Architect

The Architect Data Engineer sets the technical direction for the data engineering practice and serves as a senior-most technical authority across client engagements. Responsibilities: • Sets the technical direction for our data engineering practice and serves as a senior-most technical authority across client engagements. • Defines reference architectures and reusable IP, partner with Business Development on pursuits and proposals, and advise senior client stakeholders on enterprise data platform strategy. • Guides Lead and Senior engineers on the most complex initiatives and shapes how we deliver data engineering across the portfolio. • Designs and develops scalable ETL/ELT pipelines and data integration practices. • Implements and maintains data models (star, snowflake, data vault) to support analytics and reporting. • Sets enterprise data modeling direction and arbitrates architecture decisions across engagements. • Collaborates with data architects, analysts, and platform engineers to deploy and maintain data solutions in cloud environments (AWS, Azure, GCP). • Owns our multi-cloud data platform POV and partners with vendor and alliance teams on joint solutions. • Ensures data quality, integrity, and security through validation, monitoring, and governance practices. • Defines enterprise data quality and governance reference frameworks and advises clients on adoption. • Contributes to continuous improvement and automation of data engineering processes. • Shapes our data engineering accelerators, IP, and delivery methodology across the portfolio. • Support analytics, data science, and reporting teams with accessible, well-documented, and high-quality data. • Aligns data platform strategy with downstream AI/ML, analytics, and product roadmaps at the enterprise level. • Other duties as assigned. Requirements: • 15+ years architecting and leading enterprise data platforms across multi-cloud and multi-engagement environments. • Experience defining reference architectures, accelerators, and reusable IP for a data engineering practice. • Track record influencing senior client stakeholders (VP, CDO, CTO) on data platform strategy and investment decisions. • Experience partnering with Business Development on pursuits, scoping, proposals, and solution defense. • Experience leading and developing other architects, leads, and senior engineers across engagements. • Snowflake security architecture depth — RBAC with secondary roles, RLS, dynamic masking, Tri-Secret Secure, PrivateLink. • SOX/regulated environment experience — Actual shipped audit-ready data platforms in compliance environments. • Iceberg hands-on — Specifically the managed vs. ADLS + Polaris decision; medallion architecture in Snowflake. • Advisory/coaching capability — Track record of constructively challenging client decisions and re-architecting plans. This matters more than depth in 5 platforms. • Technical + security lead experience. • Ability to constructively challenge the planned data architecture to advocate for the proper design. Skills: • Expert SQL and data modeling at enterprise scale (performance, scalability, multi-tenant design). • Deep Python expertise for building reusable data pipeline frameworks and accelerators. • Authoritative knowledge of ETL/ELT architecture, orchestration (Airflow, dbt, Dataflow, Informatica), and streaming patterns. • Multi-cloud data platform mastery (Snowflake, BigQuery, Redshift, Databricks, Azure Fabric) including vendor and partner relationships. • Ability to define reference architectures, evaluate emerging technologies, and shape our data engineering POV. • Experience defining enterprise data quality, governance, and security frameworks (lineage, metadata, access, compliance). • Strong knowledge of infrastructure-as-code, CI/CD, and platform automation at enterprise scale. • Ability to influence C-level and VP client stakeholders on data platform strategy and business cases. • Experience supporting Business Development on pursuits, RFP responses, and architecture defense. • Track record building reusable IP, accelerators, and methodology for a data engineering practice. • Ability to lead, mentor, and develop architects, leads, and senior engineers across the portfolio. • Familiarity with AI/ML data infrastructure, real-time streaming, lakehouse, and data mesh patterns. • Delivery Methods: • Agile, hybrid, and waterfall delivery models depending on engagement context. • Experience leading multi-workstream programs and advising on delivery strategy across the portfolio. Tools: • Snowflake architecture (SnowPro Architect) • RBAC secondary roles • RLS • Masking • Tri-Secret • PrivateLink • Horizon • Iceberg (managed vs. ADLS/Polaris) + Medallion • Openflow/semantic views/Cortex • AI guardrails • SOX audit-readiness; strong advisory presence. • Snowflake • Databricks • BigQuery • DBT • Airflow • Python • Git • Terraform (or similar tools) • Tableau/Power BI • Collibra/Alation • Kafka Preferred Certifications: • Snowflake SnowPro Advanced • Databricks Certified Data Engineer Professional • AWS Data Analytics Specialty • Azure Data Engineer Associate • GCP Professional Data Engineer