Azure Data Engineer Associate, Google Cloud Platform Professional Data Engineer

The Architect Data Engineer sets the technical direction for the data engineering practice and serves as a senior-most technical authority across client engagements. They define reference architectures and reusable IP, partner with Business Development on pursuits and proposals, and advise senior client stakeholders on enterprise data platform strategy. They guide Lead and Senior engineers on the most complex initiatives and shape how business delivers data engineering across the portfolio. Core Responsibilities

• Design and develop scalable ETL/ELT pipelines and data integration practices.
• Architect: Defines reference architectures for data pipelines and integration patterns across the practice.
• Implement and maintain data models (star, snowflake, data vault) to support analytics and reporting.
• Architect: Sets enterprise data modeling direction and arbitrates architecture decisions across engagements.
• Collaborate with data architects, analysts, and platform engineers to deploy and maintain data solutions in cloud environments (AWS, Azure, Google Cloud Platform).
• Architect: Owns multi-cloud data platform POV and partners with vendor and alliance teams on joint solutions.
• Ensure data quality, integrity, and security through validation, monitoring, and governance practices.
• Architect: Defines enterprise data quality and governance reference frameworks and advises clients on adoption.
• Contribute to continuous improvement and automation of data engineering processes.
• Architect: Shapes 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.
• Architect: Aligns data platform strategy with downstream AI/ML, analytics, and product roadmaps at the enterprise level.

• Architect: 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, * 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 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, Databricks, BigQuery, dbt, Airflow, Python, Git, Terraform (or similar tools), Tableau/Power BI, Collibra/Alation, Kafka Certifications (Preferred) Snowflake SnowPro Advanced, Databricks Certified Data Engineer Professional, AWS Data Analytics Specialty, Azure Data Engineer Associate, Google Cloud Platform Professional Data Engineer