Analytics Engineer

The Analytics Engineer serves as a bridge between data engineering and data analysis, transforming raw data into curated, reusable, and trusted datasets that power analytics across the enterprise. These datasets will serve as a consistent source of truth and enable scalable, self-service analytics.

This role owns the end-to-end data workflow within an assigned domain, including data modeling, pipeline development, ELT performance, timely data delivery, quality assurance, monitoring, testing, automation, and ongoing maintenance.

The Analytics Engineer will partner with business stakeholders, data analysts, data scientists, data engineers, DevOps engineers, and architects to understand operational data and develop scalable data products. The position will also help improve the effectiveness of analysts and data scientists by providing expertise in query development, extending data models with new metrics, and promoting modern software development practices., * Collaborate with subject matter experts, data analysts, and data scientists to identify opportunities for developing well-defined, integrated, and reusable datasets.

• Transform raw data into curated data models that support reporting, analytics, and enterprise decision-making.
• Design and implement logical, physical, and dimensional data models, including fact and dimension tables.
• Develop automated, scalable, and test-driven ELT pipelines.
• Create reusable data-access patterns that reduce development time and accelerate insights.
• Apply advanced SQL to complex transformations, aggregations, analytics workflows, and performance optimization.
• Develop and maintain analytics models using dbt, including testing, documentation, and version control.
• Implement data-quality frameworks, automated validation checks, monitoring, and alerting.
• Refactor existing analytical data models to support reporting and dashboard migrations.
• Build and support data products using business intelligence, visualization, and data science tools.
• Partner with Data Engineering, DevOps, and Architecture teams to improve DataOps tools, processes, and frameworks.
• Apply modern development practices, including Git, pull requests, code reviews, CI/CD, and release management.
• Help define the analytics product roadmap in alignment with business objectives and quality outcomes.
• Partner with Data Scientists, Statisticians, and Machine Learning Engineers to implement and scale advanced solutions addressing healthcare, operational, and quality challenges.
• Evaluate technologies and recommend appropriate platforms, integrations, libraries, application servers, and frameworks.
• Participate in a shared production on-call support model.
• Contribute actively within an Agile Scrum team and help ensure successful delivery of sprint commitments.
• Independently manage multiple projects, priorities, and deadlines.

• Six or more years of experience in data, analytics, analytics engineering, data engineering, or a related field.
• Advanced SQL skills, including complex transformations, aggregations, and performance tuning.
• Strong understanding of data modeling, dimensional modeling, and data warehousing concepts.
• Experience designing and implementing fact and dimension tables.
• Hands-on experience with Snowflake or a comparable cloud data warehouse.
• Experience developing and maintaining analytics models with dbt or similar data-transformation tools.
• Experience with data integration technologies such as Informatica or Microsoft SQL Server Integration Services.
• Experience with big-data technologies such as Hadoop, Spark, Kafka, Hive, or Sqoop.
• Experience with stream-processing technologies such as Spark Streaming, IBM Streams, Flume, or Storm.
• Experience supporting business intelligence and visualization tools such as Power BI, Tableau, Qlik, or BusinessObjects.
• Experience with Linux operating systems, including RHEL or Debian.
• Ability to work with scripting languages such as Python, Bash, or Groovy.
• Experience building and consuming APIs.
• Experience with Git, code reviews, CI/CD, and release-management practices.
• Experience working within an Agile development environment.
• Strong analytical and problem-solving skills, including the ability to evaluate technical tradeoffs and recommend appropriate modeling approaches., * Eight or more years of experience in the data and analytics field.
• At least one year of experience with AWS, Microsoft Azure, or Google Cloud Platform.
• Experience using Snowflake for enterprise analytics workloads.
• Experience aligning analytical models with Power BI semantic models.
• Experience supporting BI platform migrations, such as migrations from Qlik or BusinessObjects to Power BI.
• Experience with Apache Airflow or a similar workflow-orchestration tool.
• Experience using Python for data transformation, validation, automation, or analytics workflows.
• Experience supporting healthcare, operational, or quality-related analytics.

Education

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related field, or equivalent practical experience.
• An advanced degree in Computer Science, Informatics, Information Systems, or another quantitative field is preferred.

Candidates with equivalent practical experience in analytics engineering, data modeling, data engineering, or BI development are strongly encouraged to apply. Demonstrated technical expertise and a record of successfully delivering analytics solutions will be weighted more heavily than formal education or certifications.