Data Engineer - Google Cloud Platform

What Your Impact Will Be

• Lead the development of scalable, secure, and high-performing data integration pipelines for structured and semi-structured data using Google BigQuery.
• Design and develop scalable data integration pipelines to ingest structured and semi-structured data from enterprise systems (e.g., ERP, CRM, E-commerce, Order Management) into a centralized cloud data warehouse using Google BigQuery.
• Build analytics-ready pipelines that transform raw data into trusted, curated datasets for reporting, dashboards, and advanced analytics.
• Implement transformation logic using DBT to create modular, maintainable, and reusable data models that evolve with business needs.
• Apply BigQuery best practicesincluding partitioning, clustering, and query optimizationto ensure high performance and scalability.
• Automate and monitor complex data workflows using Airflow / Cloud Composer, ensuring dependable pipeline orchestration and job execution.
• Develop efficient, reusable Python and SQL code for data ingestion, transformation, validation, and performance tuning across the pipeline lifecycle.
• Establish robust data quality checks and testing strategies to validate both technical accuracy and alignment with business logic.
• Partner with architects and Technical leads to establish best practices, scalable frameworks, and reference implementations across projects.
• Collaborate with cross-functional teamsincluding data analysts, BI developers, and product ownersto understand integration needs and deliver impactful, business-aligned data solutions.
• Leverage modern ETL platforms such as Ascend.io, Databricks, Dataflow, or Fivetran to accelerate development and improve observability and orchestration.
• Contribute to technical documentation, CI / CD workflows, and monitoring processes to drive transparency, reliability, and continuous improvement across the data engineering ecosystem.
• Mentor junior engineers, conduct peer code reviews, and lead technical Were Looking For :
• Bachelors or Masters degree in Computer Science, Data Engineering, Information Systems, or related technical field.
• Minimum 4+ years of hands-on experience in data engineering with strong expertise in data warehousing, pipeline development, and analytics on cloud experience in :
• Google BigQuery for large-scale data warehousing and analytics.
• Python for data processing, orchestration, and scripting.
• SQL for data wrangling, transformation, and query optimization.
• DBT for developing modular and maintainable data transformation layers.
• Airflow / Cloud Composer for workflow orchestration and scheduling.
• Proven experience building enterprise-grade ETL / ELT pipelines and scalable data architectures.
• Strong understanding of data quality frameworks, validation techniques, and governance processes.
• Proficiency in Agile methodologies (Scrum / Kanban) and managing IT backlogs in a collaborative, iterative experience with :
• Tools like Ascend.io, Databricks, Fivetran, or Dataflow.
• Data cataloging / governance tools (e.g., Collibra).
• CI / CD tools, Git workflows, and infrastructure automation.
• Real-time / event-driven data processing using Pub / Sub, Kafka, or similar platforms.
• Strategic problem-solving skills and ability to architect innovative solutions.
• Ability to adapt quickly to new technologies and lead adoption across teams.
• Excellent communication skills and ability to influence cross-functional teams.
• Good experience on Agile Methodologies like Scrum, Kanban, and managing IT backlog.
• Be a go-to expert for data technologies and solutions

(ref : hirist.tech)

Skills Required

Airflow, Collibra, Kafka, Sql, Pub Sub, Git, dbt, Databricks, DataFlow, Fivetran, Python