Senior Data Engineer
Cubic Corporation (company site, opens in a new tab)5+ yrsHyderabadOn-siteDirect career pageEst. 14–26 LPA
Posted 11h ago
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Job description
Structured overview of role & requirements
About This Role
- Design, build, and optimize scalable data pipelines and ETL/ELT processes using Azure Data Factory, Databricks (pyspark/spark SQL), and Python to support global analytics and reporting platforms.
- Manage and maintain enterprise data warehouse solutions, implementing dimensional modeling (star/snowflake schemas) and ensuring data quality, performance tuning, and governance across large-scale datasets.
- Collaborate cross-functionally to enable reliable datasets for KPIs, device analytics, and predictive modeling while driving improvements in automation, orchestration, and cloud data environments.
Minimum Requirements
- Bachelor's or master's degree in Computer Science, Data Analytics, Engineering, Mathematics, or related field.
- 5+ years of experience in data engineering roles with strong expertise in Azure Data Factory, Azure Databricks, Python, and SQL across Azure Synapse and Azure SQL.
- Experience in data modeling, data warehousing architectures (star/snowflake schemas, Medallion architecture), and handling complex large-scale data domains.
- Work Experience Required: Minimum 5+ years in data engineering roles.
Ideal Candidate Profile
- Experienced with cloud-based data engineering, specifically Azure ecosystem (Data Factory, Databricks, Data Lake Storage, Synapse).
- Able to take ownership of end-to-end data pipelines and analytical workstreams with minimal supervision, implementing scalable, reusable engineering and metadata-driven frameworks.
- Experienced in improving data quality, performance, and automation while supporting cross-team collaboration across analytics, operations, and engineering stakeholders.