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The TBE Data Engineering (DE) team builds and operates trusted, governed data products and pipelines that enable analytics, reporting, and downstream applications across TBE. The team partners with product, analytics, architecture, risk, and finance to deliver scalable cloud data solutions, improve data quality and observability.
Responsibilities
A Data Engineer II should possess strong hands-on experience in building and managing scalable data pipelines using modern data engineering tools and technologies such as Python, PySpark, SQL, and GCP/AWS/Azure. The candidate should collaborate with cross-functional teams to ensure alignment with business needs and enterprise standards and have a solid understanding of data processing frameworks, performance optimization, and data quality practices.
Responsibilities:
Design, build, and optimize scalable data pipelines using Python/ PySpark/ sql
Develop and maintain ETL/ELT workflows on GCP/AWS/Azure
Work with GCP services such as BigQuery/Snowflake, Cloud Storage and Dataproc/EMR/Databricks etc
Ensure data quality, integrity, and performance across data systems
Collaborate with cross-functional teams to understand data requirements and deliver solutions
Monitor, troubleshoot, and optimize data workflows
Implement best practices for data engineering, including code quality, testing, and documentation
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, and/or comparable experience; advanced degree preferred
Strong experience in Python and PySpark
Hands-on experience with Google Cloud Platform (GCP)/AWS/Azure
Good understanding of data warehousing concepts and data modeling
Familiarity with SQL and performance tuning
Knowledge of version control tools like Git
Work Experience:
2+ years of hands-on experience in data engineering, with a strong focus on building and maintaining scalable data pipelines.
Proven experience working with Python, PySpark, and SQL for data processing, transformation, and analysis.
Hands-on experience with Google Cloud Platform (GCP) or any Equivalent Cloud services such as BigQuery/Snowflake, Cloud Storage, and Dataproc/Databricks/EMR, Airflow/Composer/Astronomer etc.
Experience in designing and implementing ETL/ELT workflows in a distributed data processing environment.
Strong experience in writing optimized SQL queries and performance tuning for large datasets.
Experience in handling large-scale data processing and working with distributed computing frameworks.
Exposure to data modeling, data warehousing concepts, and schema design.
Experience with workflow orchestration tools such as Apache Airflow (or similar).
Familiarity with version control systems like Git and CI/CD practices.
Strong troubleshooting and debugging skills in data pipelines and production environments.