Lakehouse Machine Learning Engineer

ISC2

. Build and maintain Python/Spark pipelines through bronze, silver, and gold layers .

Posted 9/18/2026full-timeRemote • California • United StatesMid-LevelSenior💰 $93,000 - $118,900 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining Python/Spark pipelines, developing and deploying machine learning models, and ensuring data security and compliance. Proficient in explaining analytical results to stakeholders and managing model performance in production environments.

Highest-signal resume keywords

Python ProgrammingSQL ProficiencyMachine Learning Model DevelopmentData Security and ComplianceDatabricks Production Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Extract/Transform/Load (ETL)Survival and Time-to-Event AnalysisForecasting ModelsClassification and Propensity ModelsRecommender SystemsHyperparameter TuningModel ValidationDistributed Processing with SparkOpen Table Format (Delta, Iceberg)Cohort-Based Forecasting

Soft Skills

Stakeholder CommunicationAnalytical ThinkingCollaboration

Tools & Technologies

DatabricksMLflowScikit-learnPyTorchTensorFlowSalesforce

Certifications & Qualifications

Databricks CertificationAzure Certification

Industry Keywords

Data GovernanceData ProtectionAuditabilityModel RegistryExperiment Tracking

Tech Stack

Tools & technologies

AzureETLPythonPyTorchScikit-LearnSparkSQLTensorflowUnity

About the role

Key responsibilities & impact

Build and maintain Python/Spark pipelines through bronze, silver, and gold layers
Build semantic datasets and ML models that consume governed lakehouse data
Explore data before modeling, develop and test features, and coordinate with stakeholders on worthwhile analytical questions
Develop survival and time-to-event, forecasting, classification and propensity, sequence, recommender, and causal evaluation models
Deploy models to production and manage experiment tracking, model registry, scheduled inference, and monitoring for drift and decay
Deliver model outputs through governed semantic tables feeding dashboards and CDP systems
Explain analytical results to business teams
Take on applied LLM work, including structured extraction from free text and retrieval over governed data
Build within security and governance requirements, including access controls, data protection, auditability, and human review
Create reusable project templates, shared feature and evaluation code, and implementation standards
Develop proofs of concept to validate data support, analytical approaches, and operational viability
Perform miscellaneous duties as required

Requirements

What you’ll need

Strong Extract/Transform/Load (ETL) skills, with the ability to assemble a dataset rather than request one
Fluent Python and SQL skills
Experience working across enterprise source systems
Fluency with the standard ML stack, including scikit-learn and at least one deep learning framework such as PyTorch or TensorFlow
Knowledge of survival and time-to-event analysis, forecasting, classification and propensity, sequence models, recommenders, and causal evaluation
Familiarity with hyperparameter tuning and cross-validation
Ability to perform careful model validation, model evaluation, and bias mitigation
Ability to explain results to executives in non-technical terms
Understanding of data security, privacy, compliance, access controls, data protection, and auditability
Production experience with Databricks, including Unity Catalog, Workflows, MLflow, or comparable technologies; these are listed as a plus
Ability to perform cohort-based or hierarchical forecasting at scale, a plus
Working knowledge of Salesforce, a plus
Relevant Databricks or Azure certifications, or equivalent, a plus
Bachelor’s or Master's degree in an IT field preferred; candidates with a high school diploma or equivalent and 7+ years of hands-on experience may be considered
3+ years of hands-on experience in data engineering and applied machine learning
Experience deploying and monitoring models in production
Experience with MLflow or an equivalent tracking, registry, and scheduled-inference stack
Production experience building in a medallion architecture or equivalent layered model in a data-catalog-governed environment
Experience with distributed processing using Spark or a comparable engine
Experience with an open table format such as Delta or Iceberg
Experience with catalog-managed schemas, lineage, and access control
Practical LLM experience including embeddings, retrieval, structured extraction, and evaluation, a plus
Experience with subscription or membership-lifecycle data, a plus
Experience running build-versus-buy evaluations, a plus
Up to 5% travel may be required
This position is not available to residents of California

Benefits

Comp & perks

Up to 5% travel may be required
Work normal business hours and extended hours when necessary
Comprehensive benefits package (details linked in posting)
Inclusive and equitable work environment
Remote work environment