Job Description
- The analytics engineer acts as a bridge between a data engineer and a data analyst.
- This position is primarily responsible for modeling raw data sets into curated, reusable, trusted data sets which power analytics across the enterprise.
- These data sets will serve as the single source of truth for data and enable self-service analytics.
- In addition to the development of data models, this role is responsible for maintaining data quality within these data sets via the use of monitoring, testing, and automation.
- An additional component of the role is to improve the effectiveness of data analysts and data scientists.
- This may be via providing technical expertise in query development, extending data models via the addition of new metrics, and/or consulting on software development practices.
- The Analytics Engineer owns the entire workflow of data associated with their domain; data pipeline development, ELT performance, timely loading of data sets, and maintenance.
- This role will work within various business units and partner with data analysts and data scientists to obtain a deep understanding of operational data and develop scalable data products which empower data-driven decision making across the enterprise.
- Collaborate with business subject matter experts, data analysts, and data scientists to understand/identify the opportunities to develop well-defined, integrated, re-usable data sets which power analytics.
- Codify reusable data access patterns to speed up time to insights.
- Perform Logical and Physical data modeling with an agile mindset.
- Build automated, scalable, test-driven ELT pipelines.
- Utilize software development practices such as version control via Git, CI/CD, and release Management.
- Build data products using various visualization, BI tools and data science tools.
- Collaborate with Data Engineers, DevOps engineers and architects on improvement opportunities for DataOps tools and frameworks.
- Implement data quality frameworks and data quality checks.
- Help define analytical product roadmap to drive the business goals and superior quality outcomes.
- Work with Data Scientists, Statisticians and Machine learning engineers to implement/scale advanced algorithms to solve health care, operational and quality challenges.
- Work independently and effectively manage ones time across multiple priorities and projects.
- Make recommendations about platform adoption, including technology integrations, application servers, libraries, and frameworks.
- Participate in a shared production on-call support model.
- Be a critical part of a scrum team in an agile environment, ensuring the team successfully meets its deliverables each sprint.
- Minimum of six (6) years of experience working in data and analytics landscape.
- Strong SQL, Data Modeling and Data Warehousing fundamentals.
- Experience with software development practices; version control, code review, CI/CD.
- Experience with data integration tools: DBT, Informatica, MS Integration Services etc.
- Experience with big data toolset: Hadoop, Spark, Kafka, Hive, sqoop etc.
- Experience working with Business Intelligence Tools (Business Objects) or Visualization tools such as Qlik, Tableau, PowerBI etc.
- Experience with stream-processing systems: IBM Streams, Flume, Storm, Spark-Streaming, etc.
- Good hands-on experience with Linux (RHEL/Debian) operating system.
- Ability to code with other scripting languages such as Python, Bash, groovy etc.
- Experience consuming and building APIs.
- Experience utilizing Agile methodology for development.
- Minimum of eight (8) years of experience working in data and analytics landscape.
- One (1) year of experience working with at least one of the public cloud platforms such AWS/Azure/GCP.
- Advanced SQL for analytics engineering, including complex transformations, aggregations, and performance tuning.
- Strong dimensional data modeling skills, including design and implementation of fact and dimension tables.
- Hands-on experience with Snowflake as a cloud data warehouse for analytics workloads.
- Experience developing and maintaining analytics models using dbt, including testing and documentation.
- Proven ability to refactor existing analytical data models to support reporting and dashboard migrations.
- Experience supporting enterprise BI platforms, preferably Power BI, including semantic model alignment.
- Strong analytical and problem ? solving skills, with the ability to evaluate tradeoffs and recommend optimal modeling approaches.
- Experience with workflow orchestration tools such as Apache Airflow for scheduling and managing analytics and data transformation pipelines.
- Experience using Python for data transformation, validation, or analytics workflows (nice to have).
- Experience with Apache Airflow or similar workflow orchestration tools for scheduling and managing analytics pipelines.
- Experience using Python for data transformation, validation, automation, or analytics workflows.
- Familiarity with Agile or iterative delivery practices.
- Experience with version control and modern analytics development practices (e.g., Git, pull requests, code reviews).
- Experience supporting BI platform migrations (e.g., Qlik, Business Objects, or similar to Power BI).
- Required Education: Bachelor’s degree in Computer Science, Computer/Software Engineering, Information Technology or related fields.
- Preferred Education: Advanced degree in Computer Science, Informatics, Information Systems or another quantitative field.
- A Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, Mathematics, or a related field is helpful but not required.
- Candidates with equivalent practical experience in analytics engineering, data modeling, or BI development are strongly encouraged to apply.
- Relevant professional experience, demonstrated technical capability, and a track record of delivering analytics solutions will be weighted more heavily than formal education or certifications.
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