Design, develop, and optimize scalable data pipelines and ETL processes using Palantir Foundry for data integration, transformation, and creating data models to support analytics and business use cases.
Build and manage APIs and microservices using Python and Java to integrate data across systems and manage data processing and application logic.
Data Modeling and Architecture:
Design and implement robust, scalable, and efficient data models in Palantir Foundry.
Collaborate with data architects to define data governance, data lineage, and data quality standards.
Develop and maintain reusable pipelines and templates for data transformation and enrichment.
Azure Cloud Expertise:
Utilize Azure Synapse, Azure Data Lake, and Azure Storage for data storage and processing.
Implement secure and efficient data workflows on Azure, ensuring compliance with organizational and regulatory policies.
Monitor and troubleshoot Azure data pipelines for performance optimization.
Programming and Automation:
Write clean, maintainable, and efficient code in Python and Java for data processing, automation, and integration tasks.
Develop scripts to automate repetitive tasks and improve overall system efficiency.
Hands on Experience on Snowflake in writing procedures / functions, Snowpipe, Data Pipelines, Data Transformation.
Collaboration and Stakeholder Engagement:
Work closely with data scientists, analysts, and business stakeholders to understand data requirements and deliver actionable insights.
Provide technical support and training on Palantir Foundry to internal teams.
Participate in Agile development processes and collaborate with DevOps teams to ensure seamless deployment.
Performance Monitoring and Optimization:
Monitor data pipelines and applications for reliability, scalability, and performance.
Implement best practices for error handling, logging, and alerting to ensure system stability.
Required Skills and Qualifications:
Technical Expertise:
Proficiency in Palantir Foundry for data integration, modeling, and pipeline development.
Strong programming skills in Python and Java.
Hands-on experience with Azure Data Engineering tools (e.g., Azure Data Factory, Databricks, Synapse, Data Lake, etc.).
Solid understanding of data structures, algorithms, and software engineering principles.
Data Engineering Skills:
Experience in building and optimizing ETL/ELT pipelines for large-scale data processing.
Proficiency in SQL for data querying and transformation.
Familiarity with data governance, data lineage, and data security practices.
Cloud Expertise:
Strong knowledge of Azure cloud services and infrastructure for data engineering.
Experience with CI/CD pipelines, containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes).
Problem Solving and Collaboration:
Excellent problem-solving skills and ability to troubleshoot complex issues in data systems.
Strong communication skills to collaborate effectively with technical and non-technical stakeholders.
Preferred Qualifications:
Certification in Palantir orMicrosoft Azure Data Engineering or related cloud certifications.
Experience in working in Agile/Scrum environments.
It is the policy of AT&T to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, AT&T will provide reasonable accommodations for qualified individuals with disabilities. AT&T is a fair chance employer and does not initiate a background check until an offer is made.
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