Senior Data Engineer

Role Summary

MX is seeking a high-caliber Senior Data Engineer with a specialized focus on MLOps to architect the next generation of our financial intelligence platform. In this role, you will be the bridge between raw data and actionable AI, building the infrastructure that powers our machine learning models. You wont just move data; you will productionize ML solutions at scale using GCP, Vertex AI, and Kubernetes, ensuring our services remain resilient and low-latency in a high-stakes fintech environment.

Key Responsibilities

• Architect ML Infrastructure: Design and maintain scalable data pipelines and MLOps workflows specifically within the Google Cloud Platform (GCP) ecosystem.
• Model Productionization: Deploy, monitor, and optimize machine learning models as production-ready Vertex AI and Ray endpoints.
• Cluster Management: Orchestrate and fine-tune Kubernetes (GKE) clusters to support high-throughput data processing and real-time model serving.
• CI/CD for ML: Collaborate with Data Scientists to automate the entire ML lifecycle—from training and evaluation to seamless deployment—using Docker and modern orchestration tools.
• Real-Time Data Engineering: Build and optimize streaming pipelines (utilizing Apache Flink) and implement advanced analytical structures like Data Sketches for high-speed probabilistic analysis.
• Environment Standardization: Develop and maintain specialized Docker images to ensure consistent, reproducible environments across the full ML development lifecycle.

Technical Requirements

• Experience: 8+ years of professional experience in Data Engineering or MLOps (L4 equivalent).
• Cloud Mastery: Extensive, hands-on experience with Google Cloud Platform (GCP) is mandatory.
• Vertex AI Deep Dive: Proven expertise in the Vertex AI suite, including Pipelines, Model Registry, Feature Store, and Endpoints.
• Containerization & Orchestration: Deep technical knowledge of Kubernetes architecture and Docker best practices for production workloads.
• Programming & Data: Expert proficiency in SQL and Python is required for complex data manipulation and SDK integration.
• Streaming & Scalability: Experience handling large-scale, real-time datasets. Familiarity with Apache Flink and event-driven architectures is highly desirable.

Professional Attributes

• Fintech Mindset: Understanding of the rigor required for financial data, including idempotency, auditability, and security.
• Collaborative Leader: A track record of working effectively across multi-disciplinary teams (Data Science, DevOps, and Product).
• Problem Solver: Ability to leverage approximate computing (Data Sketches) and other advanced techniques to solve performance bottlenecks.

Education

• Bachelors or Masters degree in Computer Science, Data Science, Statistics, or a related technical field.