Data Engineer Senior

HealthPartners•Bloomington, MN

11d•Hybrid

About The Position

HealthPartners is hiring a Data Engineer. The role will support Care Delivery Finance. They are responsible for delivering a governed, scalable analytics platform that provides Finance and operational leaders with trusted, actionable insights across the organization. Our primary delivery channel is the Power BI App: Leadership Financial Reporting & Analytics, which serves as the centralized hub for financial, workforce, and operational reporting. This platform integrates data across multiple enterprise systems and presents it in a consistent, user-friendly experience to support decision-making, performance monitoring, and planning.

Requirements

Bachelor’s degree in computer science, data or social science, operations research, statistics, applied mathematics, econometrics, or a related quantitative field AND 4+ years of experience in business analytics, data science, software development, data modeling and/or data engineering work OR Master's Degree in Computer Science, Math, Software is acceptable
5+ years of programming experience with command in at least two languages: SQL, Python, Java, R or Spark
Expert proficiency in SQL; experience with Oracle, PostgreSQL, MySQL, or Microsoft SQL Server
4+ years of experience in at least one of the BI/Analytics space such as Azure Data Factory, Synapse, Data Explorer, Cosmos, App Insights, Power BI, and Databricks (or similar)
3+ years of experience in the areas of Graph databases, Big Data, or Hyper Scale products in Azure.
Experience using Git, or a comparable code repository
Must be motivated, self-driven, curious, and creative
Must be skilled communicator, and demonstrate an ability to work with end users and business leaders
Demonstrate the ability to support and complement the work of a diverse development and/or operations team
Experience working in a collaborative, cross-functional team; Active participation in sprint reviews
Expert Knowledge and a minimum of four years’ experience in the following domains: Data management, software engineering, I&O (infrastructure and operations)

Nice To Haves

Knowledge of health care operations
Exposure to agile/scrum
Ability to work in a hybrid cloud environment consisting of on premise and public cloud infrastructure.
An ideal candidate will have experience with one or more of the following skill sets
Expert in Relational databases like Oracle, SQL server
Expert in Optimizing and tuning SQL/Oracle queries, stored procedures and triggers
Expert in Python (numpy, pandas, matplotlib etc) and Jupyter notebooks for exploratory data analysis, machine learning, and process automation
Expert in areas of CI/CD, Continuous testing, and site reliability engineering.
Expert in Microsoft Azure applications such as Azure Data Factory, Synapse, Purview, Databricks /Spark, Power BI, PowerApps.
Expert in event streaming tools like NiFi, Kafka and Flink
Expert in data processing tools, like Apache’s Sqoop, Spark and Hive
Expert in Document or NoSQL datastores, particularly MongoDB
Expert in Power BI data models using advanced Power Query and DAX
Expert in in AI/ML Ops
Interest and desire to contribute to emerging practices around DataOps (CI/CD, IaC, configuration management, etc.)
Collaborate effectively with product management, program management, engineers, and stakeholders.
Excellent analytical and critical thinking skills
Ability to influence without authority and thrive in an ambiguous environment.

Responsibilities

Work with stakeholders, data scientists and analysts to frame problems, clean and integrate data, and determine the best way to provision that data on demand
Collaborate with other developers to design technology solutions that achieve measurable results at scale
Collaborate with data scientists to train, develop and operationalize learning models
Build, design and develop scalable, efficient data pipeline processes to handle data ingestion, cleansing, transformation, integration, and validation required to provide access to prepared data sets for analysts and data scientists
Evaluate and recommend technology and frameworks for building cross product data assets to optimize for flexibility, long-term viability, and time to market
Guide, mentor, influence, and adopt a cloud-first modern data architectural direction, and consistently adopt the associated standards and best practices
Anticipate the need for data governance and work with designated committees to ensure that data modeling and data handling procedures are compliant with applicable laws and policies across data pipeline.
Leads root cause analysis in response to detected problems/anomalies to identify the reason for alerts and implement advanced solutions that prevent recurring points of failure.
Applies in-depth knowledge of the business to design a data model that is appropriate for the project and translates business requirements into design specification documents to model the flow and storage of data across multiple data pipelines.
Identifies multiple, complex data sources and builds advanced code to extract raw data from identified upstream sources using query languages, tools, or machine learning algorithms, while assuring accuracy, validity, and reliability of the data across the pipeline.
Formulate techniques for quality data collection to ensure adequacy, accuracy and legitimacy of data.
Perform unit tests and conduct reviews with other team members to make sure your code is rigorously designed, elegantly coded, and effectively tuned for performance
Participate in code reviews and champion the adoption of best practices
Support the data environment by releasing new features, resolving issues for users, and working with other technology teams
Monitor and analyze information and data systems and evaluate their performance to discover ways of enhancing them (such as new technologies and upgrades).
Applies in-depth knowledge of the business to design a data model that is appropriate for the project and translates business requirements into design specification documents to model the flow and storage of data across multiple data pipelines.
Data engineers perform other duties as required, to meet team sprint goals