Principal Data Engineer

The Principal Data Engineer is responsible for designing, developing, and optimizing scalable data pipelines, data products, and cloud-based solutions that support business intelligence, advanced analytics, and AI capabilities across the enterprise. This role establishes data engineering standards, promotes best practices, and drives the development of reliable, secure, and high-performing data solutions. As a senior individual contributor, this position provides technical leadership and architectural guidance, collaborates across functional teams, and mentors engineers to support the organization’s evolving Data & AI strategy., This role follows a flexible hybrid schedule - two days per week in the office and three days working from home., Data Pipeline Engineering & Architecture

• Design, build, and maintain scalable data pipelines on AWS and Snowflake to ingest, transform, and serve enterprise data.
• Develop ELT/ETL processes using dbt, SQL, and Python to support BI, analytics, and AI use cases.
• Design curated, scalable, and AI-ready data models and data products for trusted enterprise consumption.
• Optimize pipelines for performance, scalability, reliability, and cost efficiency.

Cloud Data Platform Development

• Leverage AWS services (S3, Lambda, Glue, DynamoDB, MWAA, etc.) and Snowflake to build secure and resilient data solutions.
• Implement ingestion frameworks for batch, API, CDC, streaming, and near real-time processing.
• Design data architectures that support modern AI and machine learning workloads.
• Ensure proper orchestration, workflow management, and alignment with CI/CD and governance standards.

Data Quality, Governance & Observability

• Embed data quality checks, testing frameworks, and validation controls into pipelines.
• Support data lineage, metadata management, observability, and AI data governance initiatives.
• Troubleshoot production data issues and implement sustainable root-cause solutions.
• Partner with Data Governance to enforce enterprise data standards.

Performance, Reliability & Engineering Best Practices

• Implement CI/CD and Git-based development practices for data pipelines.
• Monitor, debug, and improve production workflows and platform reliability.
• Apply software engineering best practices including code reviews, automated testing, and infrastructure-as-code principles.
• Continuously improve operational excellence and platform scalability.

Collaboration & Technical Leadership

• Partner with BI, Analytics, AI/ML, and business stakeholders to deliver scalable data and AI solutions.
• Collaborate with AI and Data Science teams to enable model development and AI deployment.
• Mentor engineers and contribute to architecture standards and engineering best practices.
• Influence technical direction and communicate complex concepts across technical and business audiences.

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related technical field.
• 8+ years of experience in Data Engineering, Software Engineering, or related technical roles.
• Strong expertise in SQL and building scalable, complex data transformations.
• Proficiency in Python for data engineering, orchestration, and automation workflows.
• Hands-on experience designing and operating enterprise-grade data platforms on AWS and Snowflake.
• Experience with dbt or similar modern ELT/transformation frameworks.
• Proven experience building and optimizing production-grade batch and near real-time data pipelines.
• Strong understanding of dimensional modeling, curated data layers, and scalable data architecture patterns.
• Experience designing data solutions that support analytics, AI/ML, and modern AI-driven applications.
• Expertise with CI/CD, Git-based development workflows, automated testing, and infrastructure-as-code deployments.
• Strong troubleshooting, problem-solving, and performance optimization skills.
• Ability to operate effectively in fast-paced, cross-functional enterprise environments.
• Experience leveraging AI-assisted engineering and developer productivity tools to improve code quality, reliability, and delivery speed.
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Preferred Qualifications:

• Experience with data observability and data quality tools.
• Knowledge of SAP data structures (S/4, BDC data products)Experience in large-scale enterprise environments.