Senior Data Engineer

Summary

Join SAVii, a company transforming employee wellness through technology, as a Senior Data Engineer. You will be a key contributor to developing and maintaining scalable, secure, and high-performance data pipelines. Collaborate with various teams to ensure reliable data availability, design data workflows, implement data quality frameworks, and optimize data storage and retrieval. Mentor junior engineers, adhere to best practices, and proactively improve data infrastructure. This role involves designing, developing, and deploying data pipelines, collaborating on infrastructure improvements, developing workflows, contributing to data modeling, implementing data validation systems, supporting deployment, participating in code reviews, assisting junior engineers, coordinating with cross-functional teams, and troubleshooting issues. The position requires significant experience in data engineering and specific technologies.

Requirements

• 4–8 years of experience in data engineering, preferably in fast-paced, cloud-native environments
• Proven expertise in designing and building robust data pipelines using tools like Airflow, dbt, Spark, Kafka, or Beam
• Solid understanding of data warehousing, lakehouse architecture, and streaming frameworks
• Expertise in SQL and Python, or Scala, or Java
• Experience with cloud platforms, especially Google Cloud Platform (GCP). Exposure to AWS or Azure is a plus
• Familiarity with DevOps practices, version control (e.g., Git), and CI/CD tools (e.g., GitLab CI, Jenkins)
• Strong problem-solving and debugging skills
• Excellent communication skills, with the ability to clearly articulate technical concepts to non-technical stakeholders
• Experience working in Agile environments and contributing to sprint planning and reviews
• Exposure to data security, access control, and compliance practices

Responsibilities

• Design, develop, and deploy scalable ETL/ELT pipelines to ingest, transform, and store data from diverse sources such as BigQuery, MySQL, Segment, HubSpot, and Zendesk
• Collaborate with the Data Engineering Lead to implement best practices for data infrastructure, coding standards, and architectural improvements
• Develop modular and reusable workflows for both batch and streaming data processing using modern orchestration and processing tools
• Contribute to data modeling, schema optimization, and performance tuning of data warehouses and lakes
• Implement data validation, monitoring, and alerting systems to ensure high data quality and reliability across environments
• Support deployment and operationalization of pipelines using CI/CD, DevOps, and infrastructure-as-code principles
• Participate in code reviews, design discussions, and technical grooming sessions
• Assist junior engineers through peer mentoring and onboarding, fostering a culture of continuous learning and improvement
• Coordinate with cross-functional teams (Product, Decision Science, Business Operations) to understand data requirements and deliver effective solutions
• Troubleshoot pipeline and infrastructure issues, and proactively implement improvements

Preferred Qualifications

Experience with data cataloging, governance tools, or lineage tracking (e.g., DataHub, Amundsen, Collibra) is advantageous