Principal Data Engineer

Job Summary We are seeking a Principal Data Engineer to lead the design, development, and evolution of enterprise data engineering practices and platforms. This role requires a hands-on technical leader who can define standards, build scalable data solutions, and drive modernization initiatives across cloud and on-prem environments. The ideal candidate will bring strong expertise in modern data tools and architectures, along with the ability to mentor teams and deliver high-quality, analytics-ready data solutions. Key Responsibilities Define and implement enterprise data engineering standards, frameworks, and best practices Establish scalable development patterns, coding standards, and operational processes Develop and maintain documentation, runbooks, and support processes to improve operational efficiency Identify and address technical debt across data platforms Architect and deliver scalable data solutions across cloud and on-prem environments Build and optimize data pipelines, ingestion frameworks, transformation processes, and consumption layers Deliver high-quality, analytics-ready datasets for business use Write and optimize complex SQL queries and transformations Develop Python-based workflows, automation, and data processing solutions Troubleshoot and resolve performance and pipeline issues Implement DevOps/DataOps practices including CI/CD pipelines, automated testing, and deployment processes Improve reliability, scalability, and development velocity across teams Mentor and guide data engineers through design discussions, code reviews, and knowledge sharing Collaborate with business and technology stakeholders to translate requirements into scalable solutions Analyze operational workflows (e.g., service queues) to identify patterns and improve support processes Required Qualifications Bachelors degree in Computer Science, Engineering, Information Systems, or related field 8+ years of experience in data engineering or data architecture Proven experience building and scaling data engineering capabilities in enterprise environments Strong understanding of data lifecycle, data modeling, and analytics requirements Advanced SQL skills, including query optimization and performance tuning Strong experience with Python for data engineering and automation Hands-on experience with modern data tools such as SQL Server, Informatica, Azure Data Factory, Databricks, Snowflake, dbt, and Dagster Strong knowledge of ETL/ELT pipelines, data warehousing, and lakehouse architectures Experience with Git-based workflows and CI/CD practices Strong problem-solving, analytical thinking, and communication skills Ability to lead through influence, mentorship, and collaboration Ability to work in a fast-paced and evolving environment Preferred Qualifications Experience with data governance, lineage, or cataloging tools Experience designing and implementing enterprise-scale data platforms Strong leadership experience in setting technical standards and best practices Experience improving operational processes through automation and documentation

Education:

Bachelors Degree