Engineering Manager, Enterprise Data Warehouse & Analytics

machinifyinc; United States; Remote; USD 220,000 - 260,000; Full time

Benefits offered by this job

Remote work anywhere in the United S t

Medical, dental, vision coverage

Job summary

Machinify Inc. seeks a senior analytics engineering manager to lead a team building an enterprise data warehouse and analytics platform. You will split time between designing models, coding, and reviewing engineering work, while guiding data quality, governance, and semantic modeling across domains.

You will oversee Databricks pipelines, implement data contracts, and drive BI-ready semantic layers with Power BI. Remote work is available from anywhere in the United States.

Qualifications

• 10+ years in data warehousing, data engineering, or analytics engineering.
• 3+ years as hands-on engineering manager.
• Experience owning a multi-source enterprise warehouse in production.
• Strong SQL, dbt, performance tuning, tests, and CI.

Responsibilities

• Design and evolve conformed dimensional models with standards for grain and consistency.
• Build and operate Databricks ETL/ELT pipelines with incremental loads and data contracts.
• Develop governed metrics and semantic models for dashboards and self-service.
• Turn KPI definitions into certified metrics and deliver through dashboards.
• Maintain data quality via tests, reconciliation, freshness, anomaly monitoring, and CI/CD.
• Lead migration of dbt orchestration to Airflow while preserving freshness.
• Set technical plans with Product and business partners; manage and hire analytics engineers.

Skills

Dimensional modeling

Advanced SQL

dbt

Python

CI/CD

Data quality

Stakeholder communication

Distributed data design

Leadership

Mentoring

Tools

Databricks

Spark SQL

Delta Lake

Unity Catalog

dbt

Airflow

Power BI

DAX

Snowflake

BigQuery

Python

Job description

Role overview

Lead a team building a trusted enterprise data warehouse and analytics platform across multiple business domains. The warehouse is already in production, with a lakehouse, extensive transformation models and tests, BI semantic models, and governed self-service analytics. The initial focus is operational measurement, followed by a repeatable approach to extending the warehouse. This is a hands‑on management role, with roughly half the time spent designing models, coding, and reviewing engineering work.

Responsibilities

• Design and evolve conformed dimensional models, setting standards for grain and consistency across new models.
• Build and operate Databricks ETL/ELT pipelines with incremental loads, data contracts, and orchestration.
• Develop governed metrics and semantic models for dashboards, reports, and natural‑language analytics.
• Turn operational KPI definitions into certified metrics, reconcile them to source data, and deliver them through dashboards and self‑service.
• Maintain data quality through tests, reconciliation, freshness and anomaly monitoring, service objectives, and CI/CD.
• Lead the migration of dbt orchestration to Airflow while preserving data freshness.
• Set technical plans with Product and business partners, and manage, coach, and hire analytics engineers and BI analysts.

Requirements

• 10+ years in data warehousing, data engineering, or analytics engineering, including 3+ years as a hands‑on engineering manager.
• Deep dimensional modeling expertise, including fact types, conformed and slowly changing dimensions, bridge tables, and bus matrices; experience owning a multi‑source enterprise warehouse in production.
• Advanced SQL and dbt skills, including performance tuning, models, tests, macros, packages, and CI.
• Production Databricks experience with Spark SQL, Delta Lake, Unity Catalog, and medallion architecture; deep Snowflake or BigQuery experience is also considered.
• Experience building governed semantic layers and BI models; Power BI and DAX are preferred.
• Background designing production pipelines with incremental loads, snapshots or CDC, an orchestrator such as Airflow, and Python.
• Strength in data quality practices and in explaining technical decisions and trade‑offs to business partners.

Nice to have

• Healthcare claims or payment integrity data, operational KPI development, LLM‑based self‑service analytics, or experience leading teams across time zones.

Benefits and work setup

• Remote work from anywhere in the United States.
• Benefits include medical, dental, and vision coverage for employees and families, flexible time off, equity, and a 401(k) with employer match.

The listed base salary range is $220,000–$260,000.