Senior Data​/ML Engineer – Denver

Straddle is building the intelligence layer for modern payments—enabling smarter, faster, and more reliable financial decisions through data and machine learning. We operate at the intersection of fintech, data infrastructure, and real-time decisioning, where the systems we build directly impact transaction success, fraud detection, and customer experience.

We are a fast-moving, high-ownership team that values speed, clarity, and pragmatic execution. We believe in delivering impact quickly, iterating continuously, and building systems that scale as the business grows.

We are seeking a Senior/Staff ML/Data Platform Engineer to own the design and implementation of our data and machine learning platform.

This role spans data engineering, ML engineering, and MLOps, with responsibility for building a scalable lakehouse architecture, product ionizing models, and enabling real-time and batch decisioning systems.

This is a hands‑on role requiring strong individual contribution across system design, coding, and deployment. The ideal candidate can balance speed and scalability, make pragmatic trade‑offs, and operate with high ownership in a fast‑paced startup environment.

• Design and build scalable data pipelines for ingesting and processing transactional and event data
• Architect and implement a Databricks-based lakehouse using Delta Lake and Unity Catalog
• Build and maintain feature pipelines and feature store infrastructure
• Deploy machine learning models in batch and real‑time environments
• Implement CI/CD pipelines for data and ML workflows within Databricks
• Set up model monitoring, drift detection, and automated retraining pipelines
• Design real‑time and batch processing architectures based on business needs
• Develop dashboards and analytics to monitor product, model, and business performance
• Manage and optimize data infrastructure, storage, and database systems
• Translate business problems into scalable data and ML solutions
• Collaborate cross‑functionally with data science, engineering, and product teams
• Continuously improve system performance, scalability, and reliability

• 5+ years in data engineering, ML engineering, or related roles
• Strong experience building production‑grade data pipelines (ETL/ELT)
• Proficiency in R/Python and SQL
• Experience with Databricks and Apache Spark
• Experience with cloud platforms (preferably Azure)
• Experience deploying ML models into production systems
• Familiarity with CI/CD, containerization (Docker), and Dev Ops practices
• Experience with ML lifecycle tools (e.g., MLflow, Kubeflow, Vertex AI)
• Strong problem‑solving and debugging skills
• Ability to work across ambiguous, evolving requirements
• Strong communication and collaboration skills

• Databricks ecosystem (Delta Lake, Unity Catalog, MLflow)
• Data modeling, warehousing, and lakehouse architectures
• Feature engineering and feature store design
• Batch and real‑time data processing (e.g., Spark, Kafka, streaming systems)
• REST APIs / microservices for model serving
• Data quality, observability, and monitoring frameworks
• Security and compliance for sensitive financial data

• Speed over perfection — momentum creates opportunity; we deliver, iterate, and improve
• Ownership mentality — we don’t stop at “our part”; we ensure outcomes
• Honest, data‑driven thinking — we trust the data, even when it’s inconvenient
• Curiosity and creativity — we ask “why,” explore ideas, and challenge assumptions
• Pragmatic execution — we balance long‑term scalability with immediate business impact
• Collaborative mindset — we think out loud, share context, and make each other better

We are building systems that directly impact real financial outcomes. That responsibility demands high standards, strong judgment, and a bias toward action.

Apply Now

Apply now

Your application will be reviewed by us. We’ll get back to you quickly. We can’t wait to meet you!

Flexible Work Environment – We offer hybrid and remote options so you can work where you’re most productive, whether that’s at home, in‑office, or a mix of both.

Equity Ownership – As an early team member, you’ll receive equity in the form of options or RSUs—your…