trong experience in building and maintaining data pipelines, ensuring data quality, and working extensively with Google Cloud Platform (GCP), particularly BigQuery. Immediate joiners are highly preferred.
Key Responsibilities
- Design, develop, and maintain scalable data ingestion pipelines.
- Build and optimize data solutions using BigQuery and other GCP services.
- Implement data quality checks, validation rules, and monitoring processes.
- Develop and optimize ETL/ELT workflows for reliable data processing.
- Collaborate with data analysts, data scientists, and business teams to deliver data solutions.
- Troubleshoot and resolve data pipeline and BigQuery performance issues.
- Create and maintain technical documentation for data processes and models.
- Ensure data reliability, performance, and cost optimization.
Required Skills
- 4–6+ years of experience in Data Engineering.
- Strong programming skills in Python (preferred), Java, or Scala.
- Hands-on experience with Google Cloud Platform (GCP), especially BigQuery.
- Strong SQL skills and experience with data warehousing concepts.
- Experience with Apache Pinot and Elasticsearch.
- Knowledge of ETL/ELT processes and data pipeline development.
- Experience with Git or other version control systems.
- Strong analytical, problem-solving, and communication skills.
Preferred Skills
- Experience with Cloud Dataflow, Cloud Composer (Airflow), Pub/Sub, Cloud Storage, or Dataproc.
- Knowledge of data orchestration tools such as Airflow, Prefect, or Dagster.
- Familiarity with BigQuery ML and machine learning concepts.
- Understanding of data governance and security best practices in GCP.
Education
- Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field.
Pay: Up to ₹1,500,000.00 per year
Benefits:
- Cell phone reimbursement
- Commuter assistance
- Flexible schedule
- Food provided
- Health insurance
- Internet reimbursement
- Leave encashment
- Life insurance
- Paid sick time
- Paid time off
- Provident Fund
- Work from home
Work Location: In person
