Senior Data Engineer

Role Overview

• Develop and maintain scalable batch and real-time data pipelines on GCP.
• Build ingestion, transformation, and serving pipelines supporting enterprise analytics and AI use cases.
• Assist in modernization of legacy data workflows into cloud-native architectures.
• Develop reusable and maintainable data engineering components following established architectural standards.
• Support implementation of event-driven and streaming-based data processing solutions.
• Contribute to development of reusable and domain-oriented data products.
• Implement data transformation logic and standardized data models supporting downstream analytics and AI consumption.
• Support implementation of data quality validations, schema management, metadata enrichment, and reusable transformation frameworks.
• Ensure data pipelines are reliable, scalable, and production-ready.
• Work with GCP-native services including BigQuery, Dataflow, Dataproc, DBT, Pub/Sub, Cloud Storage, Cloud Composer (Airflow), and Cloud SQL.
• Develop ETL/ELT pipelines and optimize data processing workloads.
• Monitor and troubleshoot pipeline performance, failures, and operational issues.
• Support implementation of semantic models and business-friendly data structures for analytics and reporting.
• Collaborate with analytics and BI teams to improve consistency and usability of enterprise data assets.
• Assist in development of standardized metrics, dimensions, and reusable reporting datasets.
• Build and optimize AI-ready data pipelines supporting ML and GenAI initiatives.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field.
• 3–6 years of experience in data engineering and cloud-based data platform development.
• Hands-on experience working with Google Cloud Platform (GCP) data services.
• Strong SQL and Python programming skills.
• Experience developing scalable ETL/ELT pipelines and distributed data processing workflows.
• Understanding of modern data architecture concepts including data lakes, data warehouses, and streaming pipelines.
• Exposure to analytics, AI/ML, or GenAI-enabled data ecosystems preferred.
• Strong analytical, troubleshooting, and problem-solving skills.
• Ability to work collaboratively in Agile and cross-functional delivery teams.
• GCP certifications such as Associate Cloud Engineer or Professional Data Engineer are a plus.

Tech Stack

• Airflow
• BigQuery
• Cloud
• ETL
• Google Cloud Platform
• Python
• SQL

Benefits

• Health insurance
• Professional development opportunities
• Flexible work arrangements
• Paid time off
• Remote work options