Senior Data/ML Engineer

Drive Capital; Denver (CO); On-site; USD 120,000 - 150,000; Full time

Job summary

Drive Capital is seeking a Senior/Staff ML/Data Platform Engineer to design and implement the data and machine learning platform. This role includes building scalable data pipelines, deploying ML models, and collaborating with various teams to ensure data governance and performance.

The ideal candidate will have at least 5 years of experience in data or ML engineering with a strong knowledge of data processing tools and cloud platforms.

Qualifications

• 5+ years in data engineering, ML engineering, or related roles.
• Strong experience building production-grade data pipelines.
• Experience with cloud platforms (preferably Azure).

Responsibilities

• Design and build scalable data pipelines for ingesting and processing data.
• Deploy machine learning models in batch and real-time environments.
• Collaborate cross-functionally with data science, engineering, and product teams.

Skills

Data engineering

ML engineering

R/Python proficiency

ETL/ELT processes

SQL proficiency

Databricks

Apache Spark

Azure cloud platforms

DevOps practices

MLflow

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.