Senior Data Engineer - Analytics

machinifyinc; USA; Remote; USD 170.000 - 200.000; Vollzeit

Benefits dieser Stelle

US-based remote work

Zusammenfassung

machinifyinc is seeking a senior analytics engineer to turn messy operational healthcare data into trusted metrics for business leaders. The role focuses on data modeling, measurement governance, and stakeholder‑facing reporting across a unified analytics warehouse spanning multiple platforms and client workflows, with hands-on work in dbt, SQL, and Power BI.

You will collaborate with finance, operations, and client teams, own metric definitions end-to-end, and build governance around the

Qualifikationen

• 6+ years in analytics engineering, BI engineering, data engineering, or business analysis with production ownership of the models and metrics shipped.
• Expert SQL with strong dimensional modeling instincts and a reflex for spotting grain mismatches.
• Production dbt experience covering models, tests, macros, exposures, documentation, and CI.
• Hands-on experience with a cloud lakehouse or warehouse; Databricks and Spark SQL preferred.
• Demonstrated BI ownership beyond underlying tables, with Power BI and DAX strongly preferred and Tableau, Looker/LookML, MetricFlow, or Cube as credible substitutes.
• Stakeholder fluency: run requirements conversations, frame data issues as business risk, and challenge incorrect metrics diplomatically.

Aufgaben

• Run discovery sessions with business stakeholders to translate ambiguous requests into acceptance criteria and testable definitions.
• Model noisy operational data into certified dimensional models using a Databricks bronze → silver → gold architecture in dbt and SQL.
• Own metric definitions end-to-end, consolidating redundant re-derivations into a single governed semantic layer.
• Build and maintain the Power BI semantic models, dashboards, and paginated reports, including refresh health and currency.
• Implement observability for models: freshness checks, statistical anomaly detection, value-drift monitoring, and CI-enforced regression guards.
• Design and own orchestration through dbt Cloud jobs and Airflow DAGs, ensuring dependable incremental runs and correct dependency ordering.
• Document work in design docs, model documentation, and handoff packets so successors can understand choices without re-deriving them.
• Codify recurring work into AI-assisted, testable workflows and supervise outputs from coding agents.

Kenntnisse

Analytics engineering

SQL

Dimensional modeling

dbt

Power BI

Stakeholder management

Git & PR discipline

Airflow

Databricks

Spark SQL

Tools

Databricks

dbt Cloud

Power BI

Airflow

Git

Jobbeschreibung

Role overview

A senior analytics engineering position focused on turning messy operational healthcare data into trusted metrics that business leaders act on. The role is less about building pipelines and more about data modeling, measurement governance, and stakeholder-facing reporting across a unified analytics warehouse that spans multiple platforms and dozens of client workflows. Expect heavy time working alongside finance, operations, and client teams alongside deep hands-on work in dbt, SQL, and Power BI.

Responsibilities

• Run discovery sessions with business stakeholders to translate ambiguous requests into acceptance criteria and testable definitions.


• Model noisy operational data into certified dimensional models using a Databricks bronze → silver → gold architecture in dbt and SQL.


• Own metric definitions end-to-end, consolidating redundant re-derivations into a single governed semantic layer.


• Build and maintain the Power BI semantic models, dashboards, and paginated reports, including refresh health and currency.


• Implement observability for models: freshness checks, statistical anomaly detection, value-drift monitoring, and CI-enforced regression guards.


• Design and own orchestration through dbt Cloud jobs and Airflow DAGs, ensuring dependable incremental runs and correct dependency ordering.


• Document work in design docs, model documentation, and handoff packets so successors can understand choices without re-deriving them.


• Codify recurring work into AI-assisted, testable workflows and supervise outputs from coding agents.

Requirements

• 6+ years in analytics engineering, BI engineering, data engineering, or business analysis with production ownership of the models and metrics shipped.


• Expert SQL with strong dimensional modeling instincts and a reflex for spotting grain mismatches.


• Production dbt experience covering models, tests, macros, exposures, documentation, and CI.


• Hands‑on experience with a cloud lakehouse or warehouse; Databricks and Spark SQL preferred.


• Demonstrated BI ownership beyond underlying tables, with Power BI and DAX strongly preferred and Tableau, Looker/LookML, MetricFlow, or Cube as credible substitutes.


• Stakeholder fluency: run requirements conversations, frame data issues as business risk, and challenge incorrect metrics diplomatically.


• A reconciliation reflex, Git and pull‑request discipline, and comfort with high‑autonomy, async, Slack‑first environments.

Nice to have

• Healthcare data experience across claims formats (837/835/UB04), DRG/coding review, payment integrity, provider contracts, or revenue cycle.


• Data contracts, schema enforcement, ODCS‑style governance, or CI quality gating.


• Databricks Unity Catalog, lineage, and DBU cost accountability.


• Paginated reporting and Excel or SharePoint delivery for business‑formatted outputs.


• Pairing with AI coding agents and reviewing their output against a contract you wrote.

Benefits and work setup

• US‑based remote work with a digital‑first operating model.


• Full medical, dental, and vision coverage for employees and their families.


• Unlimited flexible time off and a trust‑based, autonomy‑forward culture.


• Competitive salary with equity and 401(k) with employer match.


• Listed base pay range: $170,000 – $200,000, depending on experience and skills.