Data Engineer

Our Engineering Culture:

In our Agile/Lean DevOps environment, we've nurtured a culture of innovation and experimentation across our development teams. As a customer-focused organization, we collaborate closely with our end users and product owners to understand and rapidly respond to emerging business needs. Collaboration is ingrained into every aspect of our work – from the products we develop to the world-class service we offer. We are motivated by the belief that diversity of thought, background, and perspective is crucial to crafting the finest products and experiences for our customers. Come join us and become a part of a highly ambitious team dedicated to delivering impeccable solutions!

What You'll do

As a Data Engineer at Principal Financial Group, you should have strong hands-on technical expertise and emerging leadership capabilities to join the Data & Analytics Technology team. The ideal candidate will independently deliver complex data engineering solutions while mentoring junior engineers and contributing to technical standards. This role bridges execution excellence with growing influence on architecture decisions, quality practices, and team capability development.

You'll have opportunity to:

• Experience in data engineering, particularly using AWS services like S3, Lambda, SNS, EC2, IAM and KMS, and Snowflake features like snowpipe, streams, tasks stored procedures, Snowpark. 

• Capable of handling well defined tasks and completing these tasks by writing readable, maintainable code. Adhered to coding practices and data protection standards. 

• Should have a solid understanding of BI/DW, ETL, big data, cloud, data security & protection, SQL, and data visualization concepts. 

• Develop code considering established controls, policies, recommended strategies for development work. 

• Understand basic data management process designs, models, and architectures by engaging in requirement analysis, testing, debugging, documentation, quality review. 

• Leverages AI-powered engineering tools, including GitHub Copilot and other approved GenAI solutions, to improve developer productivity, code quality, documentation, test case generation, and troubleshooting, while ensuring compliance with organizational security, privacy, and responsible AI guidelines.
• Demonstrates a mindset of AI adoption and continuous learning, proactively identifying opportunities to use AI-assisted development techniques to accelerate delivery, automate repetitive tasks, enhance data engineering workflows, and improve operational efficiency.

• Communicate assumptions, seek clarification on tasks to minimize the need for rework, and actively participate in team meetings and discussions. 

• Follow established project execution processes, coding standard and industry best practices. 

• Get actively involved in Training, self-development & knowledge sharing, Contribute actively to organization-level cloud initiatives. 

• Problem solver with analytical thinking and ability to apply root cause analysis. 

• Can easily adapt between working independently or collaboratively with a team.