GeekSoft Consulting
Data Engineer - Databricks & Treasury Data Lake
Bangalore, INVor OrtUnbefristetVollzeit
Veröffentlicht am 23. Sept. 2026
Diese Stelle wird in EN ausgeschrieben
- Help design, build and continuously improve the clients online platform.
- Research, suggest and implement new technology solutions following best practices/standards.
- Take responsibility for the resiliency and availability of different products.
- Be a productive member of the team.
Requirements
- Data Engineer with strong expertise in Databricks and Treasury Data Lake to design and develop scalable data pipelines, transform data across multi-layer architectures, and ensure high-quality, reliable datasets for downstream consumers.
- Design, build, and maintain scalable ingestion pipelines from multiple Treasury and enterprise source systems, ensuring secure and efficient data movement into the data lake.
- Transform and process data across Bronze, Silver, and Gold layers, applying best practices for data modeling, cleansing, and enrichment.
- Implement automated data quality checks, validation, monitoring, and alerting to ensure data accuracy, integrity, and reliability.
- Ensure the continuous availability and trustworthiness of datasets supporting downstream analytics, reporting, and Treasury operations.
- Continuously optimize pipeline performance, scalability, and cost efficiency across Databricks and cloud-native components.
- Hands-on knowledge and practical experience with AI technologies is mandatory.
Benefits
- A challenging, innovating environment.
- Opportunities for learning where needed.
Rollenübersicht
Jobart
Vollzeit
Erforderliche Kompetenzen
DatabricksTreasury Data Lake domain knowledgeScalable data pipeline developmentData ingestion from multiple enterprise and Treasury source systemsMedallion architecture (Bronze/Silver/Gold) data transformationData quality, validation, monitoring and alertingData availability, resiliency and reliability engineeringPerformance optimization and cost-efficient design for Databricks and cloud-native componentsHands-on AI technologies and practical AI implementationData modeling, cleansing and enrichmentSecure data movement and data security practicesTechnology evaluation and implementation following best practices and standardsTeam collaboration and being a productive team member
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