Associate Data Engineer

Blue Cross Blue Shield of Minnesota•Eagan, MN

1d•Hybrid

About The Position

The Associate Data Engineer supports the design, development, delivery, and ongoing support of enterprise data products, data integration solutions, and cloud-native data platforms. The role works with structured and unstructured data to help enable analytics, operational reporting, interoperability, and digital business capabilities through reliable data engineering solutions. This position focuses on building and maintaining modern data pipelines, cloud-native integration patterns, event-driven workflows, and data transformation capabilities under the guidance of senior engineers and architects. The Associate Data Engineer collaborates with application engineers, product owners, analysts, and business stakeholders to deliver secure, governed, and scalable data solutions. The ideal candidate brings foundational experience with data engineering concepts, SQL, Python, cloud data platforms, and distributed processing technologies, with a strong willingness to learn and grow in modern enterprise data engineering practices.

Requirements

2+ years related experience or bachelor’s degree in lieu of experience.
High school diploma (or equivalent)
Skillset and Experience Related technical, professional, internship, academic, or project experience.
All relevant experience including work, education, transferable skills, and military experience will be considered.
Exposure to data engineering, data integration, software development, analytics engineering, or data platform development.
Curiosity, willingness to learn, problem-solving mindset, strong communication skills, self-motivation, and time management skills.
Exposure to Databricks or similar cloud data platforms
Exposure to Apache Spark / Spark SQL
Familiarity with Delta Lake or similar storage formats
Python
SQL
Foundational understanding of data modeling and schema design
Familiarity with data quality, lineage, and observability concepts
Exposure to operational and analytical data platforms
Interest in data modeling and performance optimization
Exposure to AWS services such as Lambda, Glue, EMR, S3, DynamoDB, SNS/SQS, or EventBridge
Familiarity with Infrastructure as Code concepts; Terraform experience is a plus
Exposure to CI/CD concepts and tools such as GitLab, GitHub Actions, Jenkins, or similar tools
Eager to grow across data engineering, integration engineering, cloud development, and platform enablement.
Foundational experience with Databricks or cloud-based data platforms.
Experience contributing to data pipelines, integration solutions, or data transformation processes.
Exposure to modern cloud data platforms using Databricks, Apache Spark, Delta Lake, or similar technologies.
Strong SQL fundamentals with experience developing data transformations and analytical queries.
Familiarity with cloud-native platforms and distributed data processing technologies.
Able to take ownership of assigned engineering tasks and contribute to technical discussions within a team.
Collaborative technical partner who is open to coaching, feedback, and continuous learning.
Strong collaboration, problem-solving, and communication skills.
Comfortable operating in agile product teams and cross-functional environments.

Nice To Haves

Exposure to AI-assisted development tools such as Microsoft Copilot, GitHub Copilot, Claude, etc
Interest in applying AI capabilities to software development, automation, testing, and operational processes.

Responsibilities

Design, build, and support scalable cloud-based data pipelines and integration solutions that enable secure, reliable, and efficient data movement across enterprise systems.
Develop and maintain data processing, transformation, and quality capabilities using technologies such as AWS, Databricks, SQL, Spark, and Python, while following best practices for security, governance, testing, and DevOps.
Partner with business and technical teams to translate data requirements into solutions, troubleshoot issues, improve platform performance, and contribute to engineering standards, reusable frameworks, and continuous innovation.
Develop, deploy, and support data pipelines, integration solutions, and data products that support enterprise business capabilities.
Assist with implementation of cloud-native data processing solutions using AWS services, Databricks, and distributed data processing frameworks.
Develop and support batch, API, file-based, and event-driven integration patterns that enable secure and reliable data exchange.
Contribute to technical design activities for data engineering solutions, including data models, transformation logic, ingestion patterns, and consumption layers.
Develop and test data transformation logic using SQL, Spark, Python, and related technologies.
Build and support data ingestion, enrichment, quality, lineage, and observability capabilities that improve trust, transparency, and operational supportability.
Support CI/CD pipelines, infrastructure automation, testing strategies, and DevOps practices for data platforms and data products.
Partner with business stakeholders, application teams, and architects to understand requirements and translate them into technical tasks and deliverables.
Follow enterprise standards for security, governance, reliability, performance, and maintainability.
Troubleshoot data processing and integration issues while contributing to improvements in platform performance, resiliency, and operational efficiency.
Contribute to engineering standards, reusable patterns, documentation, and team best practices.
Participate in technology evaluations, proof-of-concept initiatives, and architecture reviews as directed.
Contribute to reusable integration, transformation, and data platform patterns.
Share knowledge through documentation and team learning activities.
Stay current on cloud, data engineering, AI, and interoperability technologies relevant to the role.