Senior Analytics Engineer
New
J
JobgetherHealthcare, pharmaceuticals
Based in the United StatesFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- At least 5 years of experience in analytics engineering, data engineering, or a related role delivering practical business solutions.
- Required Skills
- AWSPythonSQLMicrosoft Power BISnowflakeAzureData modelingdbt
Requirements
- At least 5 years of experience in analytics engineering, data engineering, or a related role delivering practical business solutions.
- Bachelor’s degree in Computer Science, Engineering, or a related field, or an equivalent combination of education and practical experience.
- Strong proficiency in Python, SQL, and data modeling methodologies, including Kimball and Data Vault 2.1.
- Hands-on experience with Apache Hop, dbt, Snowflake, AWS, and Azure.
- Proficiency in Power BI and DAX.
- Experience with Git and conceptual data modeling tools such as Lucidchart.
- Knowledge of pharmacy operations and translating business requirements into data models.
- Certified Data Vault 2.0 Practitioner certification.
- Experience with responsible AI adoption and identifying process and workflow improvements.
- Preferred: Certified dbt Developer certification.
- Preferred: Microsoft Power BI Data Analyst (PL-300) certification.
- Preferred: Snowflake experience or SnowPro certification.
- Preferred: AWS Data Engineering or AWS Data Analytics certification.
Responsibilities
- Lead analytics projects from problem definition and requirements gathering through solution design, deployment, and monitoring.
- Identify analytics opportunities and develop data-driven business cases outlining expected value, operational benefits, and priorities.
- Design scalable, maintainable, and cost-effective data models, pipelines, and reporting solutions.
- Investigate and resolve data quality issues, validate analytical outputs, and monitor deployed solutions.
- Improve data infrastructure and engineering workflows to increase processing efficiency and reliability.
- Maintain version-controlled code, technical documentation, and structured development practices.
- Mentor junior team members and promote consistent analytics engineering practices.
- Develop reusable tools, libraries, and shared solutions to reduce duplicated work.
- Partner with business stakeholders to clarify requirements and communicate findings through data storytelling.
- Support responsible AI adoption and identify opportunities to apply emerging technologies.
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