Senior 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 Senior 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:

• Design, build, and operationalize data pipelines, dimensional models, and data products for complex use cases.
• Develop data engineering solutions using Snowflake, AWS services, Python, SQL, and relational database technologies.
• Mentor Data Engineers through code reviews, technical guidance, and knowledge transfer on best practices.
• Collaborate with Tech Leads and Architects on solution design, architecture decisions, and technical standards.
• Implement QA and testing practices, including data reconciliation, automated SQL/Python tests, regression testing, and UAT support.
• Use approved AI-powered engineering tools, including GitHub Copilot and GenAI solutions, to improve productivity, code quality, documentation, test case generation, and troubleshooting while following organizational security, privacy, and responsible AI guidelines.
• Proactively identify opportunities to use AI-assisted development techniques to accelerate delivery, automate repetitive tasks, enhance data engineering workflows, and improve operational efficiency.
• Support product and delivery practices by contributing to story refinement, acceptance criteria definition, and dependency identification.
• Ensure adherence to data governance controls, including PII classification, access controls, auditability, lineage, and documentation.
• Follow DevOps best practices, including branching strategy, pull request standards, CI/CD pipelines, versioning, and quality gates.
• Apply analytical thinking and root-cause analysis to troubleshoot and resolve complex data issues independently.
• Communicate effectively with stakeholders by providing status updates and escalating blockers proactively.
• Drive continuous improvement in code quality, team processes, and personal skill development.

Experience: 4 to 7 years

Education:

Bachelor’s(Engineering)/Master’s in computer science or a related field.

AWS Cloud certification preferred, such as AWS Solutions Architect Associate or similar.

Snowflake SnowPro Core certification preferred.

• 5+ years of data engineering experience with increasing complexity of deliverables.
• Hands-on experience with Snowpark, Snowflake, Python, SQL, and relational databases for building scalable data transformation frameworks.
• Experience building and optimizing data pipelines, dimensional models, ETL processes, and data products.
• Experience with GitHub-based CI/CD pipelines, Airflow orchestration, quality gates, and release practices.
• Solid understanding of data quality frameworks, change data capture patterns, performance tuning, and debugging complex data issues.
• Strong communication skills with the ability to mentor junior team members and collaborate across product, engineering, and architecture stakeholders.

• Dimensional modeling, Snowflake performance tuning, and data quality checks.
• Change data capture patterns and secure data sharing or masking.
• Backfill and reprocessing using Snowpark, SQL, and Snowflake features.
• Error handling patterns and debugging of complex data issues.

• Hands-on understanding of AWS data engineering services such as S3, Lambda, Glue, SNS, EC2, IAM, and KMS.
• Experience with integration patterns between AWS and Snowflake ecosystems.
• Automated testing using SQL/Python and regression testing practices.

• Branching strategy, pull request standards, and CI checks.
• Quality gates, release notes, and versioning practices.
• Code review participation and constructive feedback.

• Oracle Database — PL/SQL development, performance tuning, data extraction
• Informatica PowerCenter — ETL workflows, mappings, reverse-engineering legacy jobs
• Enterprise Schedulers — TWS, Control-M, Autosys for dependency mapping

• PII classification, access controls, and least privilege principles.
• Auditability, lineage tracking, and business glossary understanding.
• Controls documentation awareness.

• Story refinement participation and acceptance criteria contribution.
• Backlog understanding and dependency identification.