Senior Data Engineer

What you'll do

• 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, data contracts, 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), Cloud SQL
• Develop ETL/ELT pipelines and optimize data processing workloads
• Support orchestration and scheduling of enterprise data workflows
• 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
• Contribute to metadata and data catalog integration initiatives
• Build and optimize AI-ready data pipelines supporting ML and GenAI initiatives
• Support feature engineering and data preparation workflows for AI/ML use cases
• Assist in integration with Vertex AI, BigQuery ML, Vector databases, GenAI frameworks
• Contribute to implementation of semantic search and AI-assisted data interaction patterns
• Follow established coding standards, architecture guidelines, and DevOps practices
• Participate in code reviews, testing, debugging, and performance optimization activities
• Collaborate effectively with architects, lead engineers, analysts, and client stakeholders
• Contribute to engineering documentation, operational runbooks, and technical knowledge sharing
• Continuously learn and adopt modern cloud, data engineering, and AI platform technologies
• Support implementation of monitoring, logging, lineage, and observability frameworks
• Ensure adherence to enterprise security, governance, and compliance standards
• Assist in incident resolution, root cause analysis, and platform stability improvements
• Contribute to continuous improvement initiatives for operational excellence and delivery quality

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