Data Architect

Vista Equity Partners - Austin, TX

Full-time - Senior

Austin, TX

Funds, Trusts, and Other Financial Vehicles

About the position

The Data Architect at Vista Equity Partners will lead the design and development of a robust data architecture, playing a critical role in building and optimizing the data infrastructure that supports real-time analytics and advanced business insights. This hands-on position requires coding, testing, and troubleshooting alongside data engineers and cross-functional teams to ensure high-quality delivery across the data stack.

Responsibilities

• Architect, design, and develop data pipelines and infrastructure components, ensuring solutions are scalable, secure, and efficient.
• Design and develop integrations between multiple data sources, ensuring seamless data availability across the organization.
• Build and optimize cloud-based data platforms (AWS, Azure, GCP), managing resources such as data lakes and data warehouses.
• Architect and implement real-time data pipelines for high-throughput, low-latency processing using technologies like Apache Kafka, Kinesis, or Dataflow.
• Establish and implement automated data validation and testing frameworks to ensure data accuracy and reliability.
• Collaborate with data engineers and business stakeholders to translate business needs into technical solutions.
• Design and implement data models for operational and analytical use cases, optimizing for performance in a cloud environment.
• Implement and enforce data governance best practices, ensuring compliance with regulatory standards.
• Leverage Infrastructure as Code tools (Terraform) to automate deployment and management of cloud infrastructure.
• Provide technical leadership and mentorship to junior team members.

Requirements

• 7+ years of experience in data engineering or data architecture, with at least 3 years in a hands-on technical leadership or architecture role.
• Demonstrated ability to write production-level code in Python, SQL, and big data frameworks like Apache Spark, Hadoop.
• Expert-level experience with cloud platforms (AWS, Azure, GCP) and tools like Redshift, BigQuery, Snowflake, or Synapse.
• Hands-on experience with building and optimizing data pipelines using tools like Apache Airflow, Azure Data Factory, dbt, and Fivetran.
• Experience with real-time data streaming platforms (e.g., Kafka, Kinesis, Flink) for low-latency, high-throughput data systems.
• Extensive experience in designing and implementing data models for operational databases and data warehouses.
• Proven ability to work in an Agile environment, balancing hands-on technical work with strategic decision-making.
• Deep understanding of security best practices for large-scale data platforms.

Nice-to-haves

• Hands-on experience designing data solutions that support machine learning workflows, including feature stores and real-time data feeds for model inference.