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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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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-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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- 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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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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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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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 data- driven products.
Be responsible for developing backend APIs & working on databases to support the applications.
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Develop and optimize data processing jobs on the Microsoft Azure cloud platform, leveraging services such as Azure Data Lake, Databricks, and Azure Data Factory.
Collaborate with data analysts and other stakeholders to understand data requirements and deliver high-quality data products.
Ensure data quality and integrity across all systems.
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