Company
Technology
DataEngineerSR(Databricks|ADF|AI)
Neural analysis suggests this role is
optimal for Senior candidates.
“Data Engineer SR (Databricks | ADF | AI). Skills: Databricks, Azure Data Factory, Data Pipelines, AI-driven analysis. Design scalable data models. Build scalable data models”
Industry & Context.
Analyze business requirements
What They're Looking For.
Must Have
Minimum 4 years of professional experience in IT, Relevant degree or postgraduate qualification (minimum 360h), Hands-on experience with Databricks, Hands-on experience with Azure Data Factory, Hands-on experience with ADLS, Hands-on experience with cloud-based data architectures, Solid knowledge of SQL, Solid knowledge of data modeling concepts, Proven experience in Python for data engineering, Proven experience in pipeline development, Experience with ETL/ELT tools, Experience building scalable data integration solutions, Ability to analyze business requirements, Support architects in solution design, Experience in API development, Experience in API integration, Familiarity with DevOps practices applied to data engineering
Nice to Have
Experience with SAP Datasphere, Knowledge of TIBCO, Knowledge of Knime, Knowledge of PowerCenter, Knowledge of SAP PowerDesigner, Exposure to data cataloging, Exposure to data governance, Exposure to advanced data platform ecosystems
What You'll Do.
Design scalable data models
Build scalable data models
Maintain scalable data models
Design scalable data pipelines
Build scalable data pipelines
Maintain scalable data pipelines
Develop ETL/ELT processes
Optimize ETL/ELT processes
Integrate structured data
Integrate unstructured data
Structure RAG solutions
Implement RAG solutions
Build data pipelines for API data
Transform data into analytics-ready datasets
Develop datasets for financial domains
Maintain datasets for financial domains
Support creation of data products
Support creation of reporting layers
Create APIs for data consumption
Expose APIs for data integration
Apply DevOps practices
Ensure reliable deployment
Ensure monitoring of data workflows
Ensure version control of data workflows
How You'll Work.
Team & Collaboration
Collaborate with architects; Collaborate with stakeholders
Communication Scope
Communicate with stakeholders
Full Job Description
## Accountabilities Design, build, and maintain scalable data models and pipelines using Databricks, Azure Data Factory, and Azure Data Lake Storage (ADLS). Develop and optimize ETL/ELT processes to integrate structured and unstructured data from APIs, files, and enterprise systems. Work with SAP Datasphere and relational databases (SQL Server, Oracle) to support enterprise data integration and modeling. Structure and implement RAG solutions for contractual, financial, and document-based datasets supporting AI-driven analysis. Build data pipelines to process external API data (including CXL systems), transforming it into analytics-ready datasets. Develop and maintain datasets for financial domains such as judicial deposits, guarantees, and credit-related structures. Support the creation of data products such as Position Manager and reporting layers replacing legacy BI tools. Collaborate with architects and stakeholders to refine requirements and ensure scalable, high-quality data solutions. Create and expose APIs for data consumption and integration across systems. Apply DevOps practices to ensure reliable deployment, monitoring, and version control of data workflows. Requirements: Strong hands-on experience with Databricks, Azure Data Factory, ADLS, and cloud-based data architectures. Solid knowledge of SQL (SQL Server, Oracle) and data modeling concepts. Proven experience in Python for data engineering and pipeline development. Experience with ETL/ELT tools and building scalable data integration solutions. Ability to analyze business requirements and support architects in solution design. Experience in API development and integration. Familiarity with DevOps practices applied to data engineering environments. Minimum 4 years of professional experience in IT, supported by a relevant degree or postgraduate qualification (minimum 360h). Strong communication skills and ability to collaborate with technical and non-technical stakeholders. Differentials: Experience with
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