Lead Data Software Engineer

We're currently seeking a Lead Data Software Engineer to join our organization. This role plays a central part in how the company generates, delivers, and expands data-driven insights at scale. You'll team up with talented specialists spanning multiple functions to develop creative solutions to intricate problems. It's an excellent match for someone who values pairing solid engineering discipline with genuine enthusiasm for data-informed decision making Responsibilities Assist with and contribute to the creation of machine learning datasets and oversee scoring workflows Develop and sustain thorough knowledge of both real-time and point-in-time data sources across the company, along with the Analytics staff who manage them Collaborate with Data Scientists on the development and rollout of essential machine learning models Push forward the ML dimension of the Enterprise Data Platform approach, broadening the reach of ML predictions and recommendations via API-driven scoring and warehouse-based access Weave automated processes into ML Data Engineering pipelines to enable monitoring and alert generation Implement tuning strategies and refine query performance for optimal efficiency Function as a key point of contact for the Machine Learning team on Data Lifecycle Management topics Remain informed about advancing technology trends spanning databases, data lakes, and warehouse solutions Coordinate with Analytics colleagues to adhere to documented architecture, design, and deployment guidelines, maintaining policy alignment Design and construct data models built for scalability and reuse Requirements Bachelor's or Master's degree in Computer Science, Computer Engineering, Mathematics, Statistics, or an equivalent discipline Five-plus years of practical, relevant professional background Minimum one year of experience directing and managing teams Experience within Machine Learning, Data Engineering, ML Infrastructure, or comparable domains, backed by strong grounding in probability, statistics, and machine learning fundamentals Practical experience with Databricks, including Unity Catalog, plus cloud-based ML platforms such as Azure ML Studio, AWS, or GCP Background constructing and expanding complete ML systems, ETL/ELT pipelines, and data workflows through Python, NumPy, Pandas, and SQL Experience creating scalable APIs and microservices via FastAPI, working alongside Snowflake or similar data warehouse solutions Effective communication and cross-departmental teamwork abilities, with working English fluency at B2 level or above, supporting clear comprehension of business requirements Nice to have Background deploying production-grade workloads through Docker and Kubernetes, backed by solid understanding of scalable, dependable engineering practices Working knowledge of Microsoft Azure for developing and maintaining cloud-based solutions