Senior Data Engineer

Job Overview

• As a Senior Data Engineer, you will be a core contributor to the development and maintenance of scalable, secure, and high-performance data pipelines. You’ll work under the guidance of the Data Engineering Lead and collaborate closely with product, analytics, and engineering teams to ensure reliable data availability across the organization. Your responsibilities will span designing data workflows, implementing data quality frameworks, and optimizing data storage and retrieval for analytics and reporting use cases. You’ll also play a critical role in mentoring junior engineers, adhering to engineering best practices, and proactively improving our data infrastructure.

Day-to-day Activities

• 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.

What We’re Looking For: Skills & Experience

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