VP, Data Engineering and Architecture

TaskUsSan Antonio, TX
11d

About The Position

We are seeking a hands-on Vice President of Data Engineering and Architecture to lead the strategy, design, and implementation of our enterprise data ecosystem. This person will define how data is structured, stored, and leveraged to drive analytics, automation, and AI initiatives across the organization. The ideal candidate will have deep expertise in architecting large-scale data warehouses and data lakes, with a strong command of OLAP and OLTP concepts.

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Information Systems, Data Engineering, or a related field.
  • 10+ years of experience in data engineering, architecture, or related fields.
  • Proven experience designing and implementing large-scale data warehouses and data lakes using AWS Redshift and/or SQL Server.
  • Strong understanding of OLAP and OLTP systems, data modeling, and database optimization.
  • Advanced proficiency in SQL and enterprise data integration practices.
  • Experience with Power BI or similar BI tools for enterprise reporting and visualization.
  • Excellent communication and stakeholder management skills, with the ability to bridge technical and business discussions.
  • Demonstrated success working collaboratively with automation, analytics, and software development teams.

Nice To Haves

  • Familiarity with AI/ML data preparation, semantic data modeling, and natural language query enablement is a strong plus.

Responsibilities

  • Data Architecture Leadership: Design and implement scalable, high-performance data architectures — including data warehouses (AWS Redshift, SQL Server) and structures and unstructured data lakes — that support enterprise analytics and operational excellence.
  • End-to-End Data Engineering: Lead the design, development, and maintenance of reliable data pipelines and integration processes, ensuring data is accurate, consistent, and easily accessible across business functions.
  • Performance Optimization: Apply expertise in OLAP and OLTP system design to optimize database and query performance for analytical and transactional workloads.
  • Strategic Collaboration: Partner with automation, AI, and software development teams to align data systems with business needs and integrate them into intelligent workflows.
  • Data Modeling & AI Enablement: Develop AI compatible data models that are efficient, scalable, and optimized for natural language querying, AI Integration and AI-driven analytics.
  • Analytics Enablement: Empower business teams by ensuring clean, well-structured data for Power BI and other analytics tools, driving timely and data-informed decision-making.
  • Data Governance & Quality: Establish robust data governance standards, metadata management, and quality frameworks to ensure data integrity and compliance.Operationalize a master data management framework including regular measurement and maintenance of data libraries and compliance to standards.
  • Leadership & Team Development: Build and mentor a high-performing team of data engineers and architects, fostering a culture of innovation, collaboration, and technical excellence.
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