Join a Houston, TX onsite team designing and delivering secure, stable, scalable data collection, storage, access, and analytics solutions.

Build ELT pipelines and data workflows using Python and Databricks
Use SQL frequently; apply strong understanding of NoSQL databases
Implement data security via entitlements frameworks
Update logical and physical data models as new use cases emerge
Support review of controls to ensure sufficient protection of enterprise data
Apply reuse-first, AI-assisted practices to strengthen data pipeline SDLC quality (for example, test generation and control validation)
Ensure traceability/auditability and alignment to resiliency and security expectations
Leverage enterprise-authorized AI capabilities to accelerate data pipeline and design analysis and documentation
Validate AI-assisted outputs and handle data according to sensitivity and security requirements
Review and validate AI-assisted suggestions (such as query ideas, test ideas, or model change summaries) before use; escalate when uncertain

3+ years of applied experience with software engineering concepts (formal training or certification required)
Working knowledge of AWS, Databricks, and Python across the data lifecycle
Advanced SQL skills including joins and aggregations
Working understanding of NoSQL databases
Significant experience with statistical data analysis and choosing appropriate tools and data patterns
Ability to utilize AWS Cloud Services to develop, deploy, and manage applications at scale
Proficiency in AI coding assistants, with daily use of tools such as Cursor, GitHub Copilot, and Claude for code generation, documentation, and refactoring
Effective prompt engineering: provide context, clear goals, relevant source material, and defined output expectations to produce accurate, usable code
Critical evaluation and validation of AI-generated code, including identification of hallucination patterns, security vulnerabilities, and logic errors prior to production deployment
Demonstrated experience using enterprise-authorized AI capabilities within the work environment, with strong validation habits and awareness of data sensitivity

Familiarity with standardized data layer practices (Medallion architecture)
Exposure to Aurora Postgres and MongoDB
Experience developing and supporting AWS Glue Jobs and Federated Data Lake
Skills designing efficient data models, including normalization, denormalization, and schema design; experience with relational and star schemas
Augmented development workflow using CI/CD, Docker, and AI to bridge language gaps (for example, transitioning between Python, JavaScript, or Java)

Data Engineer III - Python, Databricks

Job Description