Consultant - Data Engineer

What You'll do As a Consultant Data Engineer at Principal Financial Group, you will lead enterprise-scale data engineering delivery for our Data & Analytics Technology Program. This role will define technical direction, guide architecture decisions, mentor engineers, improve engineering culture, and drive predictable, high-quality delivery. The ideal candidate brings deep hands-on expertise in Snowflake, AWS, Python, SQL, and modern orchestration, with the ability to translate business and product priorities into resilient, governed, and scalable data products. You'll have opportunity to: Own technical direction for complex, enterprise-scale data engineering initiatives across Snowflake, AWS, and orchestration platforms. Lead architecture, design, and operational readiness for resilient, secure, scalable, and governed data products. Partner with Product Managers, Architects, SRE, and Engineering Leads to translate business priorities into feasible technical roadmaps. Guide discovery and solution-shaping by assessing feasibility, platform capability, architecture fit, data dependencies, and delivery risks. Drive engineering excellence through quality practices, delivery metrics, DevOps adoption, secure coding, automated testing, and continuous improvement. Mentor engineers, raise technical standards, and build reusable patterns, frameworks, and knowledge assets for the team. Communicate progress, risks, trade-offs, and decisions clearly to senior stakeholders.

8 to 12 years Education: Bachelor’s(Engineering)/Master’s in computer science or a related field. 12+ years of data engineering experience, including significant hands-on delivery and 5+ years in technical leadership, architecture, or lead engineer responsibilities. Deep expertise in Snowflake, AWS, Python, SQL, Airflow, GitHub, and relational data platforms. Strong capability in data architecture, dimensional modeling, pipeline optimization, data quality, and governance engineering. Proven experience designing reusable frameworks, automation patterns, CI/CD practices, and production-ready data products. Leadership in architecture reviews, technical strategy, discovery, dependency management, and delivery-risk mitigation. Proven outcome driven usage of AI-powered engineering tools, including GitHub Copilot, Snowflake Cortex, 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. Strong communication, mentoring, stakeholder influence, and cross-team collaboration skills. Must-Have Skills Data Engineering — Snowflake Snowflake dimensional modeling, performance tuning, workload optimization, and data quality controls. Hands-on experience with Snowpark for building scalable data transformation frameworks. Change data capture, secure data sharing, masking, backfill, and reprocessing patterns. Snowflake error handling, pipeline resilience, and integration with AWS services such as S3, Glue, and Lambda. Advanced SQL tuning, query profiling, clustering, partitioning, and workload management for large analytical workloads. Cloud & Platform Engineering — AWS AWS data engineering using S3, Glue, Lambda, IAM, CloudWatch, Secrets Manager, Step Functions, and EventBridge. Secure, scalable, cost-aware cloud data processing using batch, event-driven, and serverless patterns. Cloud security, encryption, access policies, observability, and operational resilience. Programming & Automation — Python / SQL Python and Snowpark for data processing, automation, APIs, reusable frameworks, testing utilities, and operational scripts. Advanced SQL for complex transformations, reconciliation, performance tuning, and analytical validation. Reusable libraries, templates, and automation patterns that improve engineering productivity. Orchestration, DevOps &