Engineering Manager – Enterprise Data Warehouse, Analytics

Machinify

. Own and evolve the conformed dimensional data model and team standards .

Posted 11/9/2026full-timeRemote • United StatesSeniorLead💰 $220,000 - $260,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in dimensional modeling, ETL/ELT pipeline design, and data quality management, with a strong focus on building and maintaining enterprise data warehouses. Proven ability to lead teams, manage technical architecture, and collaborate with stakeholders to translate business requirements into effective data solutions.

Highest-signal resume keywords

Dimensional ModelingETL/ELT Pipeline DesignSQL and dbt ExpertiseDatabricks and Spark SQLData Quality Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Dimensional ModelingSQLDbtDatabricksSpark SQLETL/ELT Pipeline DesignPythonData Quality TestingIncremental LoadsAirflow

Soft Skills

Team ManagementStakeholder CollaborationTechnical CommunicationCoachingSprint Execution

Tools & Technologies

DatabricksAirflowPower BIUnity CatalogDbt CloudMetricFlowCube

Industry Keywords

Data WarehousingAnalytics EngineeringData EngineeringData GovernanceCI/CD

Tech Stack

Tools & technologies

AirflowCloudETLPythonSparkSQLUnity

About the role

Key responsibilities & impact

Own and evolve the conformed dimensional data model and team standards
Review new models for grain and conformance
Design ETL/ELT pipelines from source systems through bronze, silver, and gold layers on Databricks
Build and maintain incremental loads, data contracts, and Airflow orchestration
Build governed metrics in Unity Catalog metric views and Power BI semantic models
Deliver certified operational KPI models and metrics reconciled to source systems
Own reconciliation, data tests, freshness and anomaly monitoring, SLOs, and CI/CD for data
Lead migration of dbt orchestration from dbt Cloud to Airflow
Partner with Product and business stakeholders on requirements
Own technical architecture, sequencing, estimates, and trade-offs
Manage, coach, and hire analytics engineers and BI analysts
Run sprint execution and establish standards for code review, testing, documentation, and AI-assisted development

Requirements

What you’ll need

10+ years in data warehousing, data engineering, or analytics engineering
3+ years as a hands-on engineering manager
Expert dimensional modeling, including grain, fact types, conformed dimensions, slowly changing dimensions, bridge tables, and bus matrix
Experience building and owning an enterprise warehouse across multiple source systems in production
Expert SQL and dbt, including complex SQL, performance tuning, models, tests, macros, packages, and CI
Production Databricks experience with Spark SQL, Delta Lake, Unity Catalog, and medallion architecture
Experience building governed metrics and semantic layers using Databricks metric views, dbt Semantic Layer/MetricFlow, Cube, or Power BI semantic models
Experience designing and running production ETL/ELT pipelines with incremental loads, snapshots or CDC, and Airflow or similar orchestrator
Python for pipeline work
Data quality experience with testing, reconciliation, observability, and CI/CD for data
Ability to work with Product counterparts and business stakeholders, translate requirements into technical designs, and explain trade-offs in plain language

Benefits

Comp & perks

Work from anywhere in the US
Full Medical/Dental/Vision for employees & their families
Flexible and trusting environment
Unlimited FTO
Equity
401(k) including employer match
Excellent healthcare
Flexible time off