Data Integration Specialist

1 day ago

Hubli, Karnataka, India beBeeEngineer Full time ₹ 12,00,000 - ₹ 15,00,000
ETL Engineer

Job Summary

We are looking for an experienced ETL expert to join our organization. The successful candidate will be responsible for designing, implementing, and managing ETL processes to integrate data from various sources.

The ideal candidate will have a strong proficiency in SQL, experience with ETL development using Informatica, and knowledge of data warehousing concepts.

The role involves working extensively with SQL to query, optimize, and manage data across systems, implementing and optimizing data models following data warehousing concepts, and developing scalable data solutions and automation scripts using Python.

Key Responsibilities:

  • Design, develop, and maintain ETL pipelines using Informatica and other tools to process and transform large datasets.
  • Work extensively with SQL to query, optimize, and manage data across systems.
  • Implement and optimize data models following data warehousing concepts (Star Schema, Snowflake Schema, Slowly Changing Dimensions, etc.).
  • Develop scalable data solutions and automation scripts using Python.
  • Leverage GCP services (BigQuery, Cloud Storage, Dataflow, Pub/Sub, etc.) for data ingestion, transformation, and storage.
  • Collaborate with data analysts, data scientists, and business stakeholders to deliver reliable datasets and insights.
  • Monitor and troubleshoot data pipelines to ensure performance and data quality.

Qualifications:

  • 3–5 years of hands-on experience as a Data Engineer or in a similar role.
  • Strong proficiency in SQL (query optimization, stored procedures, performance tuning).
  • Solid experience with ETL development using Informatica and strong knowledge of data warehousing concepts.
  • Proficient in Python programming for data processing and automation.
  • Practical experience working with GCP services, especially BigQuery.
  • Understanding of data governance, data quality, and performance best practices.

Good to Have:

  • Exposure to MLOps pipelines or integration with machine learning models.
  • Knowledge of other cloud platforms (AWS/Azure) and their data engineering services.

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