Design, build, and maintain semantic layers and KPI models using Databricks to support executive scorecards and AI-powered analytical solutions. Collaborate with business owners and engineers to define data requirements, ensure data quality, and implement robust integration pipelines.
Lead and mentor a team of data engineers to build and maintain scalable cloud-based MLOps infrastructure. Collaborate with data science teams to operationalize machine learning models and establish foundations for future agentic AI initiatives.
Lead the design and delivery of complex data pipelines and integration solutions while building and mentoring a high-performing engineering team. Own end-to-end data engineering initiatives from discovery and POC development through production deployment and optimization.
The Senior AI Engineer will design, architect, and deploy production-grade Generative AI solutions, including LLM-based systems and agentic workflows. They will collaborate with cross-functional teams to optimize AI applications for scalability, reliability, and safety while implementing best practices for model performance.
The role involves building, maintaining, and optimizing scalable data pipelines and modular code components using Databricks or Snowflake. You will also be responsible for refactoring legacy code and collaborating with cross-functional teams to ensure high-quality, performant data models for analytics.
Lead and mentor a team of data engineers to build a robust foundation for machine learning and AI initiatives. Oversee the development of scalable data pipelines, MLOps infrastructure, and production-ready deployment processes.
The Data Engineer Lead will design, develop, and maintain automated data pipelines and analytics solutions to support business intelligence and reporting. They will also oversee predictive model production processes, including model scoring and data preparation to ensure reliable operational execution.
The Data Engineer Lead will design, develop, and maintain automated data pipelines and analytics solutions to modernize reporting processes. They will also support predictive model production, including model scoring, data preparation, and ensuring reliable operational execution.
Lead end-to-end AI project delivery, including governance and stakeholder communication. Design and build robust production-ready AI systems such as RAG pipelines and agentic frameworks.
The Senior Data Governance Analyst will execute enterprise data governance initiatives, maintain standards, and perform data profiling to identify quality improvements. They will also leverage AI-powered tools for data classification, metadata management, and compliance monitoring across data assets.
The Senior Data Governance Analyst will execute enterprise data governance initiatives and maintain governance documentation, standards, and procedures. They will also leverage AI-powered tools to automate data classification, monitor compliance, and collaborate with cross-functional teams to improve data quality.
You will develop and implement Snowflake-based data governance and privacy solutions, including PII discovery and automated remediation workflows. Additionally, you will build and maintain ETL/ELT pipelines while collaborating with cross-functional teams to ensure data quality and compliance.
You will lead the design and implementation of AI-driven data governance and privacy solutions while overseeing agent-based frameworks. The role involves hands-on development in Snowflake and AWS environments to ensure secure, compliant, and high-quality data processing.
The Campaign Data Analyst will translate campaign requirements into SQL-based segmentation logic and execute workflows using established frameworks. They are responsible for validating campaign outputs through reconciliation checks and maintaining ETL workflows to ensure reliable data delivery.
Lead end-to-end AI project delivery while building and mentoring a high-performing engineering team. Define technical strategy, establish quality standards, and manage stakeholder expectations for AI system development.
Lead end-to-end AI project delivery while building and mentoring a high-performing engineering team. Define technical strategy, establish quality standards, and manage stakeholder expectations for AI system development.
Lead end-to-end AI project delivery while mentoring junior engineers and defining system strategies. Design and deploy scalable RAG systems, agentic frameworks, and LLM-powered solutions within production environments.
Lead end-to-end AI project delivery while mentoring junior engineers and defining system risks for clients. Design and build production-grade RAG systems, agentic frameworks, and scalable MLOps/LLMOps infrastructure.
You will design and implement scalable, domain-oriented data solutions and reliable pipelines on AWS. You will also collaborate with cross-functional teams to align technical solutions with business requirements and optimize data workflows.
The Senior Data Engineer will build, maintain, and optimize scalable data pipelines and modular components within Databricks to support journey analytics. This role involves refactoring legacy codebases and ensuring high-quality, performant datasets for reporting and cross-functional use cases.
Lead and mentor a team of data engineers while defining the technical strategy for Journey Analytics data platforms. Actively contribute to the design, implementation, and maintenance of scalable, high-quality data pipelines and models.
The role involves designing and implementing standardized ETL pipelines and data models to migrate data from over 80 ERP systems into Microsoft Fabric. The Lead will establish data quality frameworks and mentor team members to ensure a unified, AI-ready data architecture.
Lead the design and implementation of standardized ETL pipelines and data transformation processes to migrate ERP data into Microsoft Fabric. Establish data quality frameworks and mentor team members on engineering practices to create an AI-ready data architecture.
Design, develop, and deploy scalable AI solutions utilizing RAG, agentic frameworks, and MLOps workflows. Collaborate across engineering teams to align innovative AI systems with broader development goals and performance monitoring.
Design intuitive, enterprise-grade user interfaces and scalable design systems using Figma. Lead the end-to-end UX design for complex data review and approval workflows while collaborating with cross-functional stakeholders.
Design and implement scalable, domain-oriented data pipelines on AWS to support analytics and business-critical workflows. Collaborate with cross-functional teams to optimize data quality, reliability, and performance across various data domains.
Design and implement scalable, domain-oriented data pipelines on AWS to power analytics and business-critical workflows. Collaborate with cross-functional teams to optimize batch and streaming ingestion architectures while ensuring data quality and governance.
Design and implement scalable, domain-oriented data pipelines on AWS to support analytics and business-critical workflows. Collaborate with cross-functional teams to optimize data quality, reliability, and performance across various data domains.
Design and implement scalable, domain-oriented data pipelines on AWS to power analytics and business-critical workflows. Collaborate with cross-functional teams to optimize data quality, reliability, and performance across various data domains.
Design and implement scalable, domain-oriented data pipelines on AWS to support analytics and business-critical workflows. Collaborate with cross-functional teams to ensure data quality, reliability, and performance across various data domains.
Design and implement scalable, domain-oriented data pipelines on AWS to support analytics and business-critical workflows. Collaborate with cross-functional teams to ensure data quality, reliability, and performance across various data domains.
Design and implement scalable, domain-oriented data pipelines on AWS to support analytics and business-critical workflows. Collaborate with cross-functional teams to ensure data quality, reliability, and performance across various data domains.
Design and implement scalable, domain-oriented data pipelines on AWS to power analytics and business-critical workflows. Collaborate with cross-functional teams to optimize data quality, reliability, and performance across various data domains.
Design, develop, and maintain full-stack web applications using React and Python while integrating AWS cloud services. Collaborate with cross-functional teams to implement scalable features and optimize application performance.
Design, develop, and maintain frontend and backend components of modern web applications using React and Python. Collaborate with cross-functional teams to architect scalable cloud solutions on AWS and optimize application performance.
Design, develop, and maintain frontend and backend components of modern web applications using React and Python. Collaborate with cross-functional teams to architect scalable cloud solutions on AWS and optimize overall application performance.
Design, develop, and maintain frontend and backend components of modern web applications using React and Python. Collaborate with cross-functional teams to architect scalable cloud solutions on AWS and optimize application performance.
Design, develop, and deploy machine learning models to solve business problems while writing production-level code. Collaborate with cross-functional teams to integrate models into production and provide technical mentorship to junior team members.
Design, develop, and deploy machine learning models to solve complex business problems while writing production-level code. Collaborate with cross-functional teams to integrate models into production and implement MLOps best practices.
Design and implement a comprehensive data quality framework across Bronze, Silver, and Gold layers within Databricks pipelines. Collaborate with data engineering and business teams to validate data accuracy, perform reconciliation, and monitor pipeline health post-deployment.
The Data Quality Engineer will design and implement a data quality framework across Bronze, Silver, and Gold layers while maintaining automated checks within Databricks pipelines. They will also collaborate with Data Engineering teams to validate data flows and ensure production-ready data assets.
Design and implement a data quality framework across Bronze, Silver, and Gold layers while maintaining automated checks within Databricks pipelines. Collaborate with Data Engineering and business teams to validate data pipelines, perform reconciliation, and ensure production-ready data assets.
Design and implement a comprehensive data quality framework across Bronze, Silver, and Gold layers within Databricks pipelines. Collaborate with Data Engineering and business teams to validate data pipelines, perform reconciliation, and ensure production-ready data assets.
The Data Science Manager will lead advanced analytics initiatives, including the design and implementation of predictive models to optimize customer journeys. They will also collaborate with cross-functional stakeholders to define data-driven strategies and mentor a team of data scientists.
The QA Analyst will design and implement data quality frameworks and automated checks across Databricks pipelines to ensure data reliability. They will also collaborate with data engineers and business stakeholders to validate end-to-end data flows and support user acceptance testing.
Design and implement a data quality framework across Bronze, Silver, and Gold layers while building automated checks within Databricks pipelines. Collaborate with data engineers and business stakeholders to validate data flows and ensure production-ready data assets.
Design and implement a data quality framework across Bronze, Silver, and Gold layers while building automated checks within Databricks pipelines. Collaborate with Data Engineering and business teams to validate data flows and ensure production-ready data assets.
The QA Analyst will design and implement data quality frameworks and automated checks across Databricks pipelines to ensure data reliability. They will also collaborate with data engineers and business stakeholders to validate data assets and resolve quality incidents.
The QA Analyst will design and implement data quality frameworks and automated checks across Bronze, Silver, and Gold layers in Databricks. They will also collaborate with Data Engineering and business teams to validate data pipelines and ensure accurate, production-ready data assets.
Take ownership of complex frontend initiatives and drive them from concept to production with minimal supervision. Develop robust, reusable, and scalable components while ensuring compliance with security standards and optimizing performance.
