Engineering Manager, Middesk

• 2+ years managing data engineering and/or ML engineering teams; 7+ years of overall data engineering and/or ML engineering experiences
• Strong technical background in data infrastructure and distributed systems, with hands-on knowledge in SQL, Python, Spark, Airflow, dbt, or similar
• Experience building and scaling ML infrastructure (model training pipelines, feature stores, model serving, monitoring)
• Proven track record of delivering reliable data platforms and ML systems in production
• Ability to set technical direction, manage stakeholders, and operate with a balance of hands-on involvement and delegation
• Strong communicator who can influence both technical and non-technical audiences

Desirable

• We’re seeking an experienced Engineering Manager to lead Middesk’s data platform and machine learning infrastructure team
• This team powers both our core identity platform and new AI-driven product experiences, sitting at the heart of our product foundation and growth strategy
• As we scale to hundreds of authoritative and alternative data sources, you’ll lead efforts to reimagine data acquisition and entity resolution with AI
• We see massive opportunities to leverage AI agents to automate ETL development, monitoring, and schema adaptation—dramatically reducing the time and effort required to onboard and maintain new data pipelines
• You’ll also drive the use of LLMs and AI-driven graph techniques to significantly improve the accuracy and efficiency of our business entity resolution process, a critical component of Middesk’s platform
• You’ll manage a team of data and ML engineers, balancing hands-on technical leadership with team scaling
• You’ll work closely with Product, Design, and Go-to-Market teams to expand data coverage, accelerate ML model development, and operationalize AI/ML across the company
• Team Leadership & Management
• Manage and grow a team of data and ML engineers; set goals, provide feedback, and build a culture of technical excellence
• Partner with Recruiting to hire top talent as the team scales
• Align engineering priorities with business goals, with near-term focus on scaling data acquisition, building ML infrastructure, and improving pipeline reliability
• Data Acquisition & Engineering
• Scale Middesk’s data acquisition framework—automate evaluation of new sources, build ingestion and ETL pipelines, and integrate into entity resolution
• Partner with Product and Data Science to launch new data products and expand our business identity dataset
• Improve reliability, observability, and cost-efficiency across the platform (e.g., storage, ETL, Elastic Search)
• Machine Learning & AI Platform
• Build and scale ML infrastructure, including training pipelines, model serving, feature stores, and MLOps practices
• Partner with Data Science to accelerate experimentation and deployment across fraud, risk, and identity use cases
• Drive adoption of AI agents and LLM workflows to automate ingestion, orchestration, and AI-powered customer applications
• Cross-Functional Impact
• Collaborate with Product, GTM, and Data Science to shape the roadmap for ML- and data-driven initiatives
• Champion AI/ML adoption across the company, setting technical direction and best practices
• Partner with customers and stakeholders to ensure our data and AI infrastructure is reliable, trustworthy, and high-performing