Senior Data Engineer

Texas Capital is built to help businesses and their leaders. Our depth of knowledge and expertise allows us to bring the best of the big firms at a scale that works for our clients, with highly experienced bankers who truly invest in people’s success — today and tomorrow.  

While we are rooted in core financial products, we are differentiated by our approach. Our bankers are seasoned financial experts who possess deep experience across a multitude of industries. Equally important, they bring commitment — investing the time and resources to understand our clients’ immediate needs, identify market opportunities and meet long-term objectives. At Texas Capital, we do more than build business success. We build long-lasting relationships. 

Texas Capital provides a variety of benefits to colleagues, including health insurance coverage, wellness program, fertility and family building aids, life and disability insurance, retirement savings plans with a generous 401K match, paid leave programs, paid holidays, and paid time off (PTO). 

Headquartered in Dallas with offices in Austin, Fort Worth, Houston, Richardson, Plano and San Antonio, Texas Capital was recently named Best Regional Bank in 2024 by Bankrate and was named to The Dallas Morning News’ Dallas-Fort Worth metroplex Top Workplaces 2023 and GoBankingRate’s 2023 list of Best Regional Banks. For more information about joining our team, please visit us at www.texascapitalbank.com. 

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 and analytical platforms.
• Proficiency in advanced analytical capabilities (machine learning, predictive modeling, artificial intelligence).
• Proficiency in various algorithms, models, and frameworks. 
• 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.
• Design and develop machine learning algorithms and deep learning applications and systems
• Train, retrain, and monitor machine learning systems and models as needed
• Address planned and unplanned production issues, coordination, maintenance, communication and business support.
• Document and implement change control and best practices with regards to system maintenance, configuration, development, testing, and data integrity.
• Perform operations tasks such as monitoring application performance.
• Collaborate with other members of the engineering team to design new features.
• Identify areas for process and efficiency improvement.
• Perpetual improvement of data engineering practices to automate for manual processes.
• Partner with PMO team to run the Agile processes.
• 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.

Qualifications

• 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.
• Strong proficiency in languages like Python or R. 
• Experience in implementing machine learning models utilizing modules such as PyTorch, Scikit-learn, Panda, NumPy. TensorFlow.
• Prior experience with database and model design and segmentation techniques
• Advanced understanding of SQ