What you’ll be doing
As part of the EDA/ESE and ML team, and as part of the engineering team as a whole here at JumpCloud, you will be responsible for managing and guiding the engineering teams reporting to you.
Build End-to-End Data Pipeline Architecture: Operationalize the ingestion (Fivetran) and transformation (dbt) layers to move raw CRM (Salesforce) and ERP (NetSuite) data into our central data environment (Snowflake/Databricks) for reliable consumption.
Lead-to-Cash & Revenue Ops Alignment: Partner with Finance, Sales, and Operations to build unified reporting models spanning pipeline health, ARR, billings, churn, and Customer Lifetime Value (LTV) across Salesforce and NetSuite.
Data Governance & Modeling Standards: Enforce soft-engineering best practices for analytics (version control, automated testing via dbt, modular data modeling) to guarantee a single source of truth across executive dashboards.
Executive Stakeholder Management: Serve as a trusted advisor to C-suite leaders, presenting complex financial and operational performance metrics backed by audited data models.
You will interact effectively with the Staff and Principal engineers to build out features, help drive architecture, define best practices and enable product quality.
You will have influence over our engineering practices in addition to product strategy and execution.
As an active member of the engineering leadership team, you will partner with your peers and other teams to build, lead, and inspire a world class engineering organization.
You will provide technical leadership and oversight of team activities in your areas of expertise.
- You will be responsible for hiring, on-boarding and mentoring a growing and diverse team of high potential team members.
- What we are looking for:
- 8+ years in applied ML, including several years managing people and experience with production MLOps environment: versioned training pipelines, evaluation gates, a model registry, and automated promotion to serving
Experience managing a team of 8 or more members, performance management and building a successful team.
Experience with tools like salesforce, netsuite, dbt, fivetran, snowflake, databricks
Good Understanding on SQL
Strong understanding of software engineering principles and techniques
Track record of Software-as-a-Service Ownership, and strong proponent of Reliability Principles
Hands-on experience working with agile teams
Ability to work and communicate effectively with other engineering managers, and both technical and non-technical business stakeholders
Proven ability to thrive in a fast-moving, team-oriented, collaborative environment
Track record of continuous improvement through innovation, delivery, process development, quality, etc.
Experience leading geographically diverse engineering teams in today’s remote first work environment
Exposure to AI Coding agents like Cursor, Claude or Copilot and AI tools like Gemini, Notebook LLM and effective usage of these for day to day work optimization.
Lead ML engineers and data scientists who ship models to production, not one-off notebooks.
Set the bar for feature engineering, training, evaluation, serving, and the feedback loop that keeps models accurate as product and threat patterns change.
Hands-on fluency in Python, scikit-learn / PyTorch / TensorFlow, and large-scale data platforms (Spark, Snowflake, or equivalent); go deep on architecture when a design or incident needs it.
Partner with product, platform, and security to turn identity, device, and telemetry signals into outcomes such as precision, false-positive rate, and time-to-detect.
- Hire, coach, and sequence work across L3–L5 ICs with clear SLAs, monitoring, rollback, and on-call for model health.
- Bonus points:
- Commercial software development in a variety of languages and operating systems technologies (Golang, C++, Python, Java, etc.)
Strong technical foundation in software engineering design principles
Strong understanding of statistical and ML concepts including supervised and unsupervised learning, anomaly detection, classification, ranking/scoring, model evaluation, and handling imbalanced datasets.
