Principal Engineer

Responsibilities

• Lead the architecture, design, and implementation of scalable, secure, and high-performance data and analytics platforms across cloud and hybrid environments.
• Define and drive data engineering standards, best practices, and governance frameworks for data ingestion, transformation, modeling, and consumption.
• Partner with Product, Business, Analytics, and Platform teams to translate business requirements into robust data solutions and enterprise data models.
• Architect and oversee development of batch and real-time data pipelines using modern data stack technologies.
• Ensure data quality, reliability, observability, lineage, and governance across all data assets.
• Perform code reviews and provide technical leadership and mentorship to data engineers; establish engineering excellence and code quality standards.
• Evaluate and implement emerging technologies to enhance analytics capabilities, performance, and scalability.
• Optimize data platforms for cost, performance, and maintainability.
• Drive adoption of Dev Ops/Data Ops practices including CI/CD, automated testing, infrastructure as code, and monitoring.
• Ensure compliance with enterprise data governance, privacy, and regulatory requirements (e.g., GDPR, CCPA, PCI where applicable).
• Develop and maintain architectural documentation, data flow diagrams, and technical standards.

• 10–15+ years of experience in Data Engineering, Analytics Engineering, or Data Platform Architecture, with proven experience designing enterprise-scale data ecosystems.
• Strong programming skills in PySpark.
• Experience with tools like, Trino, Iceberg/Hudi, Apache Airflow, DBT, Git Lab, CI/CD pipelines, Docker, and Kubernetes.
• Deep expertise in cloud data platforms such as AWS (Redshift, Glue, S3, EMR).
• Strong hands‑on experience with modern data technologies (Snowflake, Spark, Kafka, Airflow, dbt, etc.).
• Advanced proficiency in SQL and programming languages such as PySpark, Python.
• Strong understanding of dimensional modeling, data warehousing concepts, data lakes/lakehouse architectures, and real-time streaming architectures.
• Experience implementing data governance, metadata management, master data management (MDM), and data quality frameworks.
• Familiarity with BI and analytics tools (Strategy, Tableau, Sigma, Looker, etc.) and supporting semantic layer design.
• Experience leading architectural decisions and influencing cross‑functional technical direction.
• Strong knowledge of distributed systems, performance optimization, and scalable system design.
• Excellent communication and stakeholder management skills; ability to articulate complex technical solutions to both technical and non‑technical audiences.
• Demonstrated leadership in mentoring engineers and driving engineering best practices.
• Familiarity of implementing AI based solutions within Data and Analytics platforms.