Lead FullStack Engineer
Lead backend architecture and API design while providing hands-on frontend development using React and TypeScript. Manage cloud infrastructure on GCP and mentor junior engineers on full-stack patterns and technical delivery.
Lead backend architecture and API design while providing hands-on frontend development using React and TypeScript. Manage cloud infrastructure on GCP and mentor junior engineers on full-stack patterns and technical delivery.
Design, build, and maintain scalable data pipelines and ingestion frameworks supporting batch, streaming, and CDC patterns. Collaborate with cross-functional teams to deliver production-ready data solutions while monitoring pipeline health and data quality.
Design, build, and maintain scalable data pipelines and ingestion frameworks using AWS services and Apache Spark. Collaborate with cross-functional teams to ensure data reliability, quality, and security across the platform.
The Lead FullStack Engineer will architect backend systems using Python and Flask while managing cloud infrastructure on GCP. They will also lead frontend development with React and TypeScript, mentoring junior engineers and ensuring high-quality technical delivery.
The Lead FullStack Engineer will architect backend systems using Python and Flask while managing cloud infrastructure on GCP. They will also lead frontend development with React and TypeScript, mentoring junior engineers and ensuring high-quality technical delivery.
The Lead Data Engineer will design and maintain scalable data pipelines and Snowflake data structures to support AI-driven proof-of-concept initiatives. This role involves collaborating with stakeholders to ensure high-quality data ingestion, transformation, and validation for downstream analytical use.
The Lead Data Engineer will design and maintain scalable data pipelines and Snowflake data structures to support a Dataiku AI agent proof of concept. They will also implement data quality validation and collaborate with stakeholders to ensure reliable, high-quality datasets for analytical workflows.
The Lead Data Engineer will build and maintain scalable ETL pipelines while implementing rigorous data quality validations. They will also manage the full data lifecycle, ensuring certified, versioned data products are delivered to stakeholders.
Build and maintain scalable ETL pipelines using AWS services to process telemetry and outage data. Ensure data accuracy through rigorous quality checks and coordinate versioned data product releases with stakeholders.
Lead backend architecture and API design while providing hands-on frontend development using React and TypeScript. Manage cloud infrastructure on GCP and mentor junior engineers on full-stack patterns and technical leadership.
Lead backend architecture and API design while providing hands-on frontend development using React and TypeScript. Manage cloud infrastructure on GCP and mentor junior engineers on full-stack patterns and technical quality.
Lead end-to-end AI project delivery while mentoring a high-performing engineering team to establish technical standards. Design and deploy robust RAG systems, agentic frameworks, and LLM-powered solutions while managing MLOps infrastructure.
Develop and maintain scalable data pipelines in Databricks to support marketing analytics and audience activation. Curate semantic layers and implement CI/CD workflows to ensure reliable, governed data products.
The Lead Data Engineer will design, build, and maintain enterprise data pipelines and ETL workflows to ensure data accuracy and accessibility. They will collaborate with stakeholders to optimize high-volume workloads and manage AWS processing resources for effective data integration.
The Lead Data Engineer will design, structure, and maintain enterprise data solutions, including data warehouses and ETL pipelines. They will lead data integration initiatives and collaborate with stakeholders to ensure data accuracy and performance for high-volume workloads.
The role involves building, maintaining, and optimizing scalable data pipelines and modular code components within Databricks. You will collaborate with cross-functional teams to ensure high-quality, performant datasets for journey analytics and reporting.
Lead the design and implementation of scalable data ingestion and transformation solutions using SQL and dbt. Develop and optimize end-to-end ELT pipelines while enforcing data quality frameworks and Medallion Architecture best practices.
The Lead Full Stack Developer will own end-to-end technical delivery, including backend architecture, frontend development, and cloud deployments. They will also mentor engineers, define technical standards, and integrate AI-assisted workflows to improve productivity and code quality.
The Lead AI Engineer will architect and deploy production-grade Generative AI solutions, including LLM-based systems and agentic workflows. They will collaborate with cross-functional teams to ensure scalability, reliability, and the implementation of Responsible AI practices.
Analyze campaign and marketing data to identify trends and performance drivers while developing analytical reports using Python and SQL. Collaborate with business stakeholders to translate analytical needs into actionable insights and support data-driven decision-making.
You will design, develop, and maintain scalable data pipelines to ingest and transform data from multiple sources. Additionally, you will collaborate with cross-functional teams to ensure data accuracy, standardization, and reliability for analytics workflows.
Lead end-to-end AI project delivery and mentor a high-performing engineering team to establish technical standards. Design and deploy robust AI systems, including RAG frameworks and LLM-powered solutions, while managing stakeholder expectations and business initiatives.
The Marketing Enablement Manager will create sales enablement materials and coordinate cross-functional projects to bring marketing solutions to market. They will manage project timelines, facilitate stakeholder communication, and support the development of business development presentations and training resources.
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.
You will lead the design and implementation of scalable data ingestion architectures and ELT pipelines using Snowflake and AWS. Additionally, you will enforce data quality frameworks and provide technical leadership to ensure reliable, governed data workflows.
Design and build scalable backend systems while leading the architecture of distributed, high-performance data processing environments. Mentor junior engineers and collaborate with cross-functional teams to ensure system health and performance through robust observability and monitoring.
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 structures.
Design, develop, and optimize machine learning models and recommendation systems to drive business impact. Collaborate with cross-functional teams to deploy scalable solutions using MLOps practices and cloud technologies.
Design and build scalable backend systems using Java while leading the architecture of distributed, high-performance data processing environments. Manage containerized workloads via Kubernetes and collaborate with cross-functional teams to define service contracts and system observability.
Design and build scalable backend systems using Java while leading the architecture of distributed systems for large-scale data processing. Manage containerized workloads using Kubernetes and implement observability solutions to ensure system health and 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.
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.
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 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 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, 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.
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.
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 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.
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 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.
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.