Hybrid Data Analytics Engineer — Databricks & Quality

McKesson; Irving (TX); Híbrido; USD 112.000 - 186.000; Jornada completa

Descripción de la vacante

McKesson is hiring a Data Analytics Engineer to design and own scalable data solutions for US Oncology datasets. You will work in Databricks using Python and SQL to deliver governed, reusable data assets, focusing on data quality, lifecycle management, and reduced technical debt.

You will build consolidated datasets (patients, doctors, employees) within a medallion architecture, collaborate with stakeholders, and document procedures for long-term reliability.

Formación

• Degree or equivalent and typically requires 4+ years of relevant experience.
• 4+ years of experience in data engineering, analytics engineering, software development, computer science, or a related field, including experience delivering and supporting production data solutions.
• 4+ years of experience developing and optimizing data solutions in Databricks or Snowflake; Databricks preferred.
• 4+ years of experience with Python and SQL for data pipeline, validation, automation, and application development.
• Hands-on experience implementing data quality controls, monitoring, issue remediation, and reusable validation frameworks.
• Experience with dimensional modeling, star schemas, database design, data warehousing, and consolidated enterprise data assets.
• Ability to translate business rules into governed technical solutions and communicate complex concepts clearly to non-technical stakeholders.
• Strong understanding of data governance, security, quality, lineage, retention, purge controls, and production support best practices.

Responsabilidades

• Data Quality Engineering: Design and implement reusable validations for completeness, accuracy, timeliness, consistency, and compliance with business rules; incorporate controls into regular pipeline monitoring and validation.
• Technical Debt Reduction: Identify and resolve high-impact technical debt by standardizing engineering patterns, simplifying data flows, and improving the reliability, scalability, and supportability of existing solutions.
• Data Lifecycle Management: Design, test, document, and implement a governed, auditable purge process that supports retention expectations, reduces unnecessary storage, and protects downstream dependencies.
• Consolidated Data Assets: Design and build trusted, reusable datasets, including consolidated patient, doctor, and employee tables, using agreed business definitions, governance standards, and validation rules.
• Dimensional Modeling: Develop and maintain scalable star-schema models and supporting technical specifications to improve query performance, simplify data consumption, and enable consistent metric definitions.
• Data Integration and Platform Engineering: Build and optimize data pipelines within a medallion architecture using Databricks, Python, APIs, and cloud services, ensuring secure, reliable, and cost-efficient data flow across systems.
• Documentation and Operational Readiness: Maintain data quality rules, lineage, technical designs, operating procedures, and support handoff materials to ensure long-term maintainability, compliance readiness, and effective production support.
• Collaboration and Delivery: Partner with data engineers, analysts, business stakeholders, and internal or third-party teams to clarify requirements, align definitions, communicate risks, and deliver measurable improvements while supporting committed project and production needs.

Conocimientos

Databricks

Python

SQL

Data quality controls

Data governance

Dimensional modeling

Cloud platforms (Azure)

Data integration

Herramientas

Power BI

Tableau

Looker

Jira

Asana

Descripción del empleo

McKesson is hiring a Data Analytics Engineer to design and own scalable data solutions for US Oncology datasets. You will work in Databricks using Python and SQL to deliver governed, reusable data assets, focusing on data quality, lifecycle management, and reduced technical debt.

You will build consolidated datasets (patients, doctors, employees) within a medallion architecture, collaborate with stakeholders, and document procedures for long-term reliability.