Lead, Data AI – Engineering

Murphy-Hoffman Company (MHC Kenworth)

. Architect and build MHC’s core data platform across ingestion, transformation, storage, and serving layers for batch and streaming workloads .

Posted 9/21/2026full-timeRemote • Minnesota • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and architecting data platforms, with a strong focus on data engineering, AI/ML feature development, and team leadership. Proficient in establishing data quality and governance practices while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

Data EngineeringAI/ML Feature DevelopmentSQLPythonCloud Data Warehousing

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data ModelingAPI DesignTestingCI/CDData TransformationData GovernanceMetrics and PredictionsDocument UnderstandingModel EvaluationEmbedding/Vector Search

Soft Skills

Communication SkillsTeam LeadershipProduct Sense

Tools & Technologies

SnowflakeBigQueryDatabricksRedshiftDbtAirflowDagsterPrefectAWSGCP

Industry Keywords

B2B SaaSDocument AINLPData PrivacySOC 2GDPR

Tech Stack

Tools & technologies

AirflowAmazon RedshiftAWSAzureBigQueryCloudGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

Architect and build MHC’s core data platform across ingestion, transformation, storage, and serving layers for batch and streaming workloads
Model data for analytics and product use cases, creating documented reusable datasets and a trusted semantic layer
Establish data quality, lineage, observability, and governance practices
Build pipelines and feature/embedding stores for analytics, ML, and LLM applications
Turn operational and document data into metrics, signals, predictions, and document understanding
Build and evaluate models and retrieval systems, including RAG over documents
Partner with Product to design and ship LLM-powered features from prototype to production with guardrails, evaluation, and cost controls
Manage, mentor, and grow a team of 2–4 data and AI engineers
Set technical standards and a pragmatic data and AI roadmap
Make build-vs-buy decisions and balance speed with maintainability
Collaborate with Product, Engineering, and business stakeholders
Review code and designs, prototype difficult problems, and maintain engineering quality
Deliver a prioritized roadmap and early data-layer wins within 90 days
Establish a reliable data foundation and first AI/LLM-powered feature within six months
Build a steady stream of data products and AI features within one year

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Information Security, or a related field or equivalent years of experience
7+ years building data and/or backend systems in production, with recent hands-on experience in both data engineering and applied AI/ML
Strong SQL and Python
Experience with a cloud warehouse/lakehouse such as Snowflake, BigQuery, Databricks, or Redshift
Experience with transformation tools such as dbt
Experience with orchestration tools such as Airflow, Dagster, or Prefect
Hands-on experience building production AI/ML features
Practical experience with LLMs, including prompting, RAG, embeddings/vector search, evaluation, and integrating models via APIs or open-source models
Software engineering fundamentals including data modeling, API design, testing, CI/CD, and reliable systems on AWS, GCP, or Azure
Experience leading or mentoring engineers and interest in growing a team
Strong product sense and communication skills
Preferred: experience with document AI/intelligent document processing or NLP over unstructured text
Preferred: experience operating LLMs in production, including evaluation/observability, prompt and cost optimization, fine-tuning, or agentic workflows
Preferred: background in B2B SaaS, document/process automation, fintech, or other data-heavy enterprise domains
Preferred: familiarity with data privacy, security, and compliance such as SOC 2 and GDPR
Preferred: experience with streaming, infrastructure-as-code, and MLOps/LLMOps tooling
Candidates must be based in the United States
This role is not eligible for visa sponsorship

Benefits

Comp & perks

Flexible, work-where-you-live model
401(k) plan with deferred and Roth options and employer match of 50% up to a maximum of 4.5% of gross pay
Comprehensive medical plans with co-pay or HSA coverage options
Dental and vision plans
Daycare and Medical FSA/HSA
Group term life insurance coverage of $50,000
Generous paid time off (PTO) policies
Employee Assistance Program (EAP)
Additional life insurance
Critical illness insurance
Accident, cancer and hospital indemnity insurance
Legal/ID Shield
Pet insurance
Four weeks of paid paternity leave after one year of employment, with partial eligibility beginning at six months, where no state paid leave program applies
Twelve weeks of paid leave for the birth parent, eligibility rules apply