About this opportunity
Bonfirevc lists this Senior Data/ML Engineer opportunity in denver, Colorado. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Company Overview
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.
Position Overview
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, productionizing 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.
Essential Functions
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
Establish data governance standards (access control, lineage, data quality, compliance)
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
Desired Experience & Skills
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 DevOps practices
Experience with ML lifecycle tools (e.g., MLflow, Kubeflow, Vertex AI)
Strong problem‑solving and debugging skillsAbility to work across ambiguous, evolving requirements
Strong communication and collaboration skills
Technical Expertise
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
Performance optimization for large‑scale data systems
Security and compliance for sensitive financial data
Culture Fit
At Straddle, data science and engineering are guided by a shared philosophy:
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.
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Worksite address
denver, CO, 80285, US
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.