Engineering Manager, Data Platform and Machine Learning

Middesk

full-time

Posted on: 9/9/2025

Origin: • 🇺🇸 United States • California

✨ AI Apply

💰 $160,000 - $250,000 per year

SeniorLead

AirflowDistributed SystemsETLGoPythonSparkSQL

About the role

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, focusing on scaling data acquisition, building ML infrastructure, and improving pipeline reliability
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 business identity dataset
Improve reliability, observability, and cost-efficiency across the platform (storage, ETL, Elastic Search)
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
Collaborate with Product, GTM, and Data Science to shape roadmap for ML- and data-driven initiatives
Champion AI/ML adoption across the company, set technical direction and best practices
Partner with customers and stakeholders to ensure data and AI infrastructure is reliable, trustworthy, and high-performing
Follow hybrid work model with expectation of 2 days per week in SF office

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
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
Must be based within a commutable distance to San Francisco (expectation of 2 days per week in SF office)
Nice to haves: Experience in fintech, fraud/risk, compliance, or B2B SaaS
Nice to haves: Previous experience scaling data acquisition frameworks or integrating third-party data sources
Nice to haves: Familiarity with graph database solutions, graph feature engineering, and AI agent services and deployment in production
Nice to haves: Experiences with entity resolution processes
Nice to haves: Startup experience building teams and systems from 0→1 and scaling early-stage infrastructure
Nice to haves: Background collaborating with go-to-market and customer-facing teams on technical products