The Data Engineer is responsible for reviewing the approved User Requirements Study (URS) and Data Warehouse Design (DWD) documents and producing a comprehensive ETL Mapping Design Document for an Oracle Exadata Data Warehouse environment.
The ETL Mapping Design will serve as the technical specification for ETL developers to build data integration processes that load, transform, validate, and maintain data within the Oracle Exadata Data Warehouse.
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Read, interpret, and refactor complex data pipelines to improve performance, readability, and maintainability across existing data infrastructure.
Maintain and improve Python data pipeline scripts that extract, transform, and load data between SAP, Salesforce, ServiceMax, SFTP, Email, and MES systems, ensuring reliability and performance.
Build and publish new Power BI reports and dashboards from initial requirements through final delivery, including data modeling, DAX measures, and visual design.
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Work with business owners and data stewards to define business metadata and critical data elements. Maintain glossaries, lineage, classification schemas, and domain structures. Maintain repository of enterprise data assets and domains.
Coordinate, schedule, and run the Data Governance Council (DGC) meetings and working sessions.
Support audits, assessments, and remediation actions.
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- Design, build, launch and maintain efficient and reliable large-scale batch and real-time data pipelines with data processing frameworks.
- Integrate and collate data silos in a manner which is both scalable and compliant.
- Collaborate with Project Manager, Data Architect, Business Analysts, Frontend Developers, Designers and Data Analyst to build scalable datadriven products.
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-Design, build, launch and maintain efficient and reliable large-scale batch andreal-time data pipelines with data processing frameworks.
-Integrate and collate data silos in a manner which is both scalable andcompliant.
-Collaborate with Project Manager, Data Architect, Business Analysts, FrontendDevelopers, Designers and Data Analyst to build scalable data driven products.
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-Design, build, launch and maintain efficient and reliable large-scale batch andreal-time data pipelines with data processing frameworks.
-Integrate and collate data silos in a manner which is both scalable andcompliant.
-Collaborate with Project Manager, Data Architect, Business Analysts, FrontendDevelopers, Designers and Data Analyst to build scalable data driven products.
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-Design, build, launch and maintain efficient and reliable large-scale batch andreal-time data pipelines with data processing frameworks.
-Integrate and collate data silos in a manner which is both scalable andcompliant.
-Collaborate with Project Manager, Data Architect, Business Analysts, FrontendDevelopers, Designers and Data Analyst to build scalable data driven products.
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Uncover insights and present findings and recommendations to multiple levels of stakeholders, creating visual displays to communicate quantitative information.
Keep abreast of latest analytical techniques and identify appropriate data driven predictive analytics from use case concept through experiment design to industrialization/deployment to end users.
Work collaboratively with key business and technology teams to ensure that strategic initiatives are delivered successfully and that the solutions delivered are aligned to the planned enterprise IT framework.
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Assist in the development and enhancement of AI-powered chatbots using GCP services such as Dialogflow, Vertex AI, Firestore or other relevant platforms
Involve in Retrieval-Augmented Generation (RAG) techniques to improve chatbot relevance using enterprise documents. Participate in testing and refining chatbot performance to ensure accuracy and user satisfaction
Research and explore the concepts and applications of Agentic AI, focusing on how it can be implemented using GCP tools and services (e.g., Vertex AI Agents, custom models
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Build and manage the data asset using some of the most scalable and resilient open source big data technologies like Airflow, Spark, Apache Atlas, Kafka, Yarn, HDFS, ElasticSearch, Presto/Dremio, HDP, Visualisation layer,Snowflake and more.
Design and deliver the next-gen data lifecycle management suite of tools/frameworks, including ingestion and consumption on the top of the data lake to support real-time, API-based and serverless use-cases, along with batch (mini/micro) as relevant
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Foundational Modeling: Focus on structural data modeling (e.g., Star Schema, Data Vault) rather than predictive modeling to create a "single source of truth" for the department.
Tooling & Integration: Evaluate and deploy the core data stack—selecting the right warehouses, orchestration tools, and integration layers to support Business Analysts and functional teams.
Experience: 5+ years in Data Engineering or Data Architecture, ideally within a high-stakes environment like commodity trading or fintech.
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