Platform Engineer

Data Reliability Engineer Responsibilities Configure and maintain data pipelines, observability tooling, and reliability frameworks within assigned data platforms and products. Participate in gathering business requirements and defining technical requirements for data quality, reliability, and availability solutions. Design and implement data integrations across multiple vendor-hosted and cloud data platforms, leveraging APIs, event-driven architectures, and custom-developed pipeline solutions. Administer data access security roles and profiles, including creating new roles, managing data governance policies, and enforcing prescribed data access permissions. Design operational data workflows and propose areas for process optimization, including SLA/SLO definition and data freshness monitoring. Perform new feature exploration of data platforms and tooling to determine feature-fit for strategic and tactical data reliability program activities. Provide business and IT support for data platform and pipeline-related incidents, including root‑cause analysis and collaborating with multiple teams across TCB. Configure or develop low‑code and code‑first solutions leveraging vendor data platforms to improve data reliability and reduce time‑to‑detection on data incidents. Document and implement change control, and follow best practices for data system maintenance, configuration, development, testing, and migration. Perform operational tasks such as monitoring data pipeline performance, data quality metrics, and anomaly detection, and implement solutions for reliability improvements. Collaborate with data engineering, analytics, and platform teams for the development, support, and continuous improvement of data reliability capabilities. Follow software engineering and DataOps practices to develop automation solutions for manual data validation, reconciliation, and incident response processes. Champion implementation of innovative data reliability technologies, inclusive problem‑solving approaches, outside‑of‑the‑box thinking, teamwork, self‑organization, and diversity. Qualifications Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or related field. Minimum 3 years of relevant experience in data engineering, data operations, or a related data technology domain. Foundational knowledge of the practical application of data engineering principles, including data pipeline design, data modeling, and implementation of data reliability frameworks. Foundational knowledge and understanding of diverse data platforms and ecosystems, including relational databases, cloud data warehouses (e.g., Snowflake, Redshift), and streaming platforms (e.g., Kafka). Ability to obtain, analyze, and synthesize information from multiple data sources to identify reliability gaps, data quality issues, and systemic risks. Ability to utilize industry‑standard project management, work management, and reporting tools in a data‑driven delivery context. Analytical mindset focused on results, with strong critical thinking, root‑cause analysis, and data‑driven problem‑solving and decision‑making skills. Proficiency in organization and time‑management skills with a proven track record of meeting incident response and project delivery deadlines. Proficiency in written and verbal communication skills, alongside strong data interpretation and visualization skills to convey reliability metrics and trends to stakeholders. Proficiency in the broader data toolset including SQL, Python, Power BI, dbt, data cataloging tools, and observability/monitoring platforms; familiarity with MS Office suite (Outlook, Teams, Word, PowerPoint, Excel, Visio). Foundational knowledge of data monitoring practices, including data quality dimensions (completeness, accuracy, timeliness, consistency), alerting, and SLA tracking. Foundational knowledge of coding or scripting languages, particularly SQL and Python, for pipeline automation, data validation, and incident remediation. Ability to test data pipeline functionality through regression, reconciliation, and smoke testing practices. Fundamental understanding of data reliability engineering principles, including SLIs, SLOs, error budgets, and data observability frameworks. Essential functions of this role are listed above; other duties may be assigned as per standard departmental policy. TexasCapitalBank is an Equal Opportunity Employer. #J-18808-Ljbffr