Data Engineer Master

Texas Capital•Dallas, NC

About The Position

The Data Engineer Master is responsible for designing, constructing, and maintaining systems for data collection, storage, access, and analytics at Texas Capital Bank — including the data foundations that power reporting, advanced analytics, and AI. The successful candidate will collaborate with cross-functional teams to ensure data quality, optimize data workflows, and drive data driven decision making across the firm. We are seeking qualified candidates with a passion for innovation that have extraordinary levels of critical thinking, motivation, and initiative and aspire to deliver superior client experiences.

Requirements

Minimum 8 years of relevant experience in an appropriate technology domain.
Bachelor's degree in Computer Science, Engineering, Information Systems or Technology, or related field.
Advanced knowledge of the practical application of engineering science and technology, including data management principles, techniques, procedures, and equipment to the design and implementation of products.
Advanced understanding of SQL and hands-on coding in at least one data engineering language (Python, Scala, Java).
Advanced knowledge and understanding of diverse data platforms, operating systems, cloud solutions, current and emerging technologies.
Knowledge of architecture and development of a modern data ecosystem: batch and streaming ingestion, data modeling, lakehouse and warehouse storage, and analytical solutions.
Advanced knowledge of data governance practices, including lineage, data classification, and controls over sensitive and PII data in analytics and AI use cases.
Experience delivering data assets through version control and automated deployment (Git plus any CI/CD toolchain).
Subject Matter Expertise (SME) on three or more platforms in appropriate technology domain.
Ability to obtain, analyze and synthesize information from multiple sources.
Ability to provide guidance and solution design principles to all engineers.
Ability to drive technology updates and implementations across data platforms.
Analytical mindset, focused on results with critical thinking, research and problem-solving, and decision-making skills.
Proficiency in organization and time management skills with proven track record of meeting various deadlines.
Proficiency in written and verbal presentation skills alongside strong data interpretation and visualization skills.
Proficiency in the use of the broader MS Office suite (Outlook, Teams, Word, PowerPoint, Excel, Project, Visio) and an enterprise BI platform such as Power BI.

Nice To Haves

Cloud lakehouse platforms (Microsoft Fabric, Databricks, Snowflake, or equivalent) and open table formats (Delta Lake, Iceberg).
Transformation and orchestration as code (dbt or SQLMesh, with Azure Data Factory, Airflow, or Dagster).
Streaming and change data capture (Kafka/Event Hubs, Debezium, Spark Structured Streaming, or Flink).
Automated data quality and observability (dbt tests, Great Expectations, Soda, Purview, or Unity Catalog).
Production ML and generative AI data work: feature pipelines and model registries (MLflow, Azure ML), or retrieval pipelines, document parsing, embeddings, and vector/hybrid search with document-level access control.
Governed, AI-ready data access: semantic models and metric layers (Power BI semantic models, dbt Semantic Layer, Cube), and exposing data to AI agents via APIs or Model Context Protocol (MCP).
Modernizing legacy ETL estates (SSIS, stored-procedure or scheduler-driven workloads) onto a cloud or lakehouse target.
Productive use of AI coding assistants (GitHub Copilot, Claude Code, Cursor) with sound judgment about review, security, and licensing.
Experience in working in an AI-native product model.

Responsibilities

Practical application of engineering science and technology, including applying principles, techniques, procedures, and equipment to the design and implementation of data warehouse and advanced analytical applications.
Responsible for enhancing and maintaining TC's data warehouse, lakehouse, and analytical platforms.
Design and build batch and streaming pipelines across structured, semi-structured, and unstructured data.
Support the engineering needs associated with advanced analytical capabilities (machine learning, predictive modeling, artificial intelligence), including training data, feature pipelines, and the retrieval and search data layers behind AI applications.
Embed data quality, validation, lineage, and automated testing into delivery as code; leverage debugging and testing processes and protocols to finalize code.
Perform new feature exploration of each solution to determine feature-fit for strategic and tactical program activities.
Address planned and unplanned production issues, including monitoring of pipeline and application performance, maintenance, coordination, communication, and business support.
Document and implement change control and best practices with regards to system maintenance, configuration, development, testing, and data integrity.
Collaborate with other members of the engineering team to design new features.
Identify areas for process, efficiency, and platform cost improvement.
Perpetual improvement of data engineering practices to automate manual processes, including use of AI-assisted development tooling within the bank's review, security, and change management standards.
Partner with PMO team to run the Agile processes.
Provide end-to-end solutions for LOBs.
Lead the craftsmanship, availability, resilience, and scalability of your solutions.
Bring a passion to stay on top of tech trends, experiment with and learn new technologies, participate in internal & external technology communities, and mentor other members of the engineering community.
Encourage innovation, implementation of cutting-edge technologies, inclusion, outside-of-the-box thinking, teamwork, self-organization, and diversity.