Analytics Engineer II; Epic Cogito, Power BI

Position: Analytics Engineer II (Epic Cogito, Power BI)
Job Title & Specialty Area:
Analytics Engineer II (Epic Cogito, Power BI)

Department:
Childrens Spclty Pharm

Location:

Dallas, TX

Shift: 8:00 am to 5:00 pm

Job Type: Remote

Why Children's Health?

At Children's Health, our mission is to Make Life Better for Children, and we recognize that their health plays a crucial role in achieving this goal.

Through our cutting-edge treatments and affiliation with UT Southwestern, we strive to deliver an extraordinary patient and family experience, ensuring that every moment, big or small, contributes to their overall well-being.

Our dedication to promoting children's health extends beyond our organization and encompasses the broader community. Together, we can make a significant difference in the lives of children and contribute to a brighter and healthier future for all.

Summary:

Under the direction of data & analytics engineering leadership, the Analytics Engineer II will own and operationalize analytics artifacts for one or more domains (e.g. pharmacy, pediatrics service line, supply chain), build and maintain metrics stores/semantic layers, improve reuse and performance of data models, and enable domain adoption in a federated model. This role will work closely with operational stakeholders, product team members, and data stewards to ensure successful delivery of analytics products that support business goals.

Analytics engineers bridge data engineering and analytics development to deliver analytics-ready data assets, semantic layers, metric stores, analytics models/marts, and operationalized analytics products for clinical and operational domains at Children's Health. This role family supports domain-aligned analytics, federated CI/CD and lifecycle management for analytics artifacts, and close collaboration with clinical, financial, supply chain and operational stakeholders.

Responsibilities:

Core Responsibilities:

* Prepare and model analytically ready datasets; implement and maintain semantic layers, metrics stores or data marts; apply data engineering best practices to analytics (version control, CI/CD, environment management); collaborate with BI analysts, data scientists and domain stakeholders to operationalize analytics products; ensure alignment with governance and data quality requirements

Level Specific Requirements:

* Design and implement semantic layer components (metrics store objects, analytical models, data marts) to support domain KPIs and BI consumption

* Implement automated testing, CI/CD pipelines and deployment promotion for analytics products; own environment management for domain artifacts

* Lead data quality monitoring for analytics products and collaborate with data stewards to remediate issues; maintain metadata and lineage in the catalog

* Optimize query and semantic performance for BI tools; implement best practices to reduce dashboard sprawl and inconsistent metrics

* Collaborate with clinical and operational stakeholders to translate clinical workflows and EHR/ERP data into repeatable analytics products (e.g., procedure-level cost and outcomes dashboards, inventory visibility reports)

* Domain collaboration (e.g. clinical, financial, or supply chain), stakeholder facilitation and problem-solving skills

* Ability to translate business KPIs into reproducible metrics-as-code and communicate technical concepts to a wide range of stakeholders

* Ability to mentor junior engineers

Healthcare domain considerations:

* Work with EHR/clinical data and supply‑chain/ERP data sources, follow interoperability expectations (FHIR/HL7 awareness for clinical integrations), support clinical analytics use cases (procedural analytics, outcomes measurement, preference card analytics) and supply‑chain analytics (spend, contract, inventory visibility) when relevant

Engineering practices to measure and apply:

* Version control of analytics content, separate dev/test/prod environments, workflow orchestration, metadata/cataloging and performance testing to improve model uptime, adoption, code quality and defect resolution

Governance & risk:

* Adhere to organizational data governance, privacy and security requirements (e.g., HIPAA-relevant controls), and collaborate with…