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- Create, maintain and optimize data pipelines as workloads move from development to production for specific use cases.
- Manage data pipelines through stages, beginning with ingestion of data sources through integration to consumption for specific use cases.
- Utilize innovative tools, techniques and architectures to partially or completely automate tasks in order to minimize manual processes, reduce the potential for error and improve productivity.
- Assist with the renovation of data management infrastructure that supports automation in data integration and management.
- Partner with other Information Systems teams, business data analysts and other data and analytics consumers to refine their data requirements for initiatives and consumption.
- Train data and analytics consumers about data pipelines and preparation techniques to make it easier for them to integrate and consume the data they need for their own use cases.
- Apply understanding of data and domains to address emerging data requirements.
- Propose innovative data ingestion, preparation, integration and operationalization techniques to optimally address data requirements.
- Promote available data and analytics capabilities and expertise to IS staff and department leaders.
- Collaborate with and educate staff and leadership on how to leverage data and analytics capabilities to achieve business goals.
- Propose and implement process improvements.
- Meet deadlines for completion of workload.
- Maintain agreed upon work schedule.
- Demonstrate cooperation and teamwork.
- Provide cross-training on specific job responsibilities.
- Meet identified business goals that contribute to departmental goals.
- Perform other duties as needed.
- Strong ability to design, build and manage data pipelines for data structures encompassing data transformation, data models, schemas, metadata and workload management
- Strong ability to work with IT and business staff to integrate analytics and data science output into business processes and workflows
- Strong ability to partner with data science teams to leverage data science and refine and optimize machine learning models and algorithms
- Strong ability to collaborate with data governance, quality and security experts to move data pipelines into production in compliance with applicable standards and certification
- In depth knowledge of commonly used database programming languages for relational databases (e.g. SQL)
- In depth knowledge of commonly used cloud-based data warehouse platforms (e.g. Snowflake, Redshift, etc.)
- Ability to work across multiple deployment environments including cloud, on-premises and hybrid
- Ability to work with multiple operating systems and containerization platforms (e.g. Docker, Kubernetes, AWS Elastic Container Service, etc.)
- Ability to develop using Microsoft Azure products (e.g. Data Factory, Functions, Databricks, Monitor, etc.)
- Ability to work with large, heterogeneous datasets to build and optimize data pipelines, pipeline architectures and integrated datasets
- Ability to extract business value while considering automation opportunities
- Adept in the use of traditional data integration technologies including ETL/ELT, data replication/CDC and API design and access
- Strong ability to work with and optimize existing ETL/ELT processes and data integration, data preparation flows and helping to move them into production
- Strong ability to work with analytics tools for object-oriented/object function scripting using R, Python, Java, Scala and/or similar languages
- Understanding of business intelligence solutions including working knowledge of commonly used data discovery, analytics and BI software tools for semantic layer-based data discovery (e.g. Tableau, Power BI, etc.)
- Strong ability to apply Agile methodologies
- Ability to apply DevOps practices and tools and DataOps principles to data pipelines to improve data flows
- Knowledge of emerging data ingestion and integration technologies
- Possess curiosity and desire for ongoing learning about new data initiatives and how to address them
- Ability to continually learn the latest versions of development tools and software products
- Excellent written and oral communication skills
- Ability to successfully manage multiple tasks, concurrent high priority projects and continuous deadlines
- Possess a high degree of initiative, motivation, self-discipline and good judgment
- Knowledge of the basic concepts of managed care preferred
- Knowledge of health insurance business entities, relationships and processes preferred
- Minimum 5 years’ experience in data management required in roles that included all or most of the following functions:
- Utilization of data integration, modeling, optimization and data quality improvement processes
- Design of data and information architecture that delivers large (multi-terabyte) enterprise data warehouse solutions integrating many heterogeneous data sources
- Development utilizing tools such as Microsoft SQL Server, Snowflake and/or similar tools
- Development using Microsoft Azure products such as Data Factory, Functions, Databricks, Monitor and/or similar products
- Data warehouse technical development that encompasses the data management life cycle and establishes end-to-end data warehousing, data management and analytics architecture
- Data management experience within the healthcare industry preferred.