Data Engineer - Global Team (India)
Build and maintain end-to-end data pipelines and create AI-ready analytical datasets. Collaborate with stakeholders to incorporate business logic and optimize pipelines for reliability and performance.
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Build and maintain end-to-end data pipelines and create AI-ready analytical datasets. Collaborate with stakeholders to incorporate business logic and optimize pipelines for reliability and performance.
Provide L3 engineering support for Palantir Foundry and Foundation environments, focusing on resolving complex technical issues and software defects. Maintain production pipelines, platform workflows, and contribute to platform governance and stability.
The role involves leading the development and maintenance of data services and solutions to support products, downstream services, or infrastructure tools used across BEES. This includes designing efficient data models and implementing architectural improvements to enhance performance, monitoring, and evolution of data products.
The role involves designing, developing, and rapidly deploying scalable AI solutions to transform data into strategic capabilities for national security challenges. Responsibilities include developing new AI methods, rapid prototyping of models, and taking full accountability for client problems from discovery through production deployment.
The Senior Solutions Architect will provide technical leadership post-sales to guide strategic customers in designing and implementing big data projects, from architectural design to data engineering and model deployment. This role involves architecting production-level workloads, performance testing, optimization, and delivering training sessions to promote community adoption.
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The role involves independently implementing, optimizing, and maintaining robust ETL/ELT pipelines using various technologies like Python, Airflow, Spark, and AWS services. Responsibilities also include engaging in collaborative design sessions, supporting testing strategies, and guiding the team on CI/CD practices.
The Tech Lead will own the design, building, and delivery of high-quality AI/ML solutions, with a strong emphasis on Generative AI and LLMs. This involves hands-on development, technical leadership, and mentoring AI engineers on best practices across AI engineering and MLOps.
The Solutions Architect partners with sales to design and deliver data-driven solutions, translating complex business requirements into scalable architectures leveraging AI, analytics, and cloud technologies. They act as a trusted technical advisor, guiding customers, leading workshops, and developing technical narratives to support sales strategies.
This role involves defining and scaling the modern data and analytics platform architecture, ensuring it is secure, scalable, governed, and optimized for performance and cost using AWS as the core environment. The Director will establish foundational architectural frameworks, technology standards, and best practices to enable analytics, AI, and domain-driven data products across the enterprise.
The Senior Data Engineer will be pivotal in building a robust data platform using Databricks to support skilled data professionals analyzing and improving a highly visible online product. Responsibilities include architecting and building scalable data platforms in AWS and Databricks, designing data pipelines, and optimizing infrastructure.
Design and implement core platform services exposed through high-quality APIs and SDKs for a unified data and AI/ML model development platform. Partner closely with AI/ML researchers, data engineers, and product teams to deliver a paved-path developer experience.
The AWS Engineer will design, build, and maintain scalable data pipelines and analytics platforms on AWS. This includes transforming raw data into analytics-ready datasets and ensuring data quality and compliance.
Design, build, and maintain scalable data pipelines that support analytics and reporting. Collaborate with cross-functional teams to define data requirements and deliver actionable insights.
The Staff Data Engineer is responsible for designing and evolving core components of onX’s lakehouse and data platform, focusing on data structure, governance, and security. They lead complex initiatives and provide technical guidance and mentorship to other engineers.
The Data Architecture Lead will be responsible for shaping the enterprise data foundation by translating architecture standards into domain-aligned designs, defining consistent data structures, and partnering with engineering and analytics teams for implementation and adoption. This role enables trusted data products, analytics, operational reporting, and future AI use cases through modern data architecture practices.
Responsibilities include presenting and analyzing data using charts, tables, and Excel, alongside interpreting financial reports and market performance to derive investor-focused takeaways. Analysts will also interpret factors like yield, price, and risk for asset merits and conduct deep quantitative risk modeling.
As a Senior Data Engineer, you will deliver on complex projects, either individually or by leading small teams. You will manage customer relationships and support pre-sales processes while mentoring junior engineers.
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The Data Engineer will design, optimize, and own data pipelines that scrape, process, and ingest transaction and listing data. They will also build monitoring systems to track data metrics and continuously improve the data infrastructure.
The Data Engineer will design, develop, and maintain scalable ETL pipelines and data workflows to ingest and transform data into modern cloud platforms, primarily utilizing Azure Databricks and lakehouse architectures.
The Manager of Data Engineering leads a team to build and maintain scalable data pipelines and models for enterprise reporting and analytics. This role emphasizes delivering data across a hybrid architecture, particularly focusing on a modern cloud data lakehouse platform.
Design and architect scalable applications and systems using MongoDB. Collaborate with the sales team to drive account success and ensure customer satisfaction.
The Director of Strategy & Analytics will lead a small team to conduct analyses of operational trends and provide actionable insights. They will also drive data analysis as an individual contributor and collaborate with various teams to define tests and communicate results.
The Staff Machine Learning Engineer will craft, implement, and maintain MLOps tools and practices while optimizing model performance and scalability. They will also build tools to improve the lives of data scientists and contribute to the design and architecture of ML systems.
Own the analytics strategy for self-serve growth by designing experiments and building measurement infrastructure. Collaborate with product managers and designers to define success metrics and drive insights that increase conversion rates.
Implement ETL/ELT solutions and maintain data pipelines for large volumes of data. Collaborate with teams to ensure data security and optimize data processing systems.
The Data Analyst will lead data pipeline projects and mentor junior engineers. They will be responsible for building and optimizing ETL/ELT pipelines and collaborating with cross-functional teams.
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The Databricks Developer will build and maintain data pipelines using Apache Spark on Databricks and ensure data handling follows security and governance standards. They will also investigate and fix issues in production environments.
The Curriculum Manager will manage the content development lifecycle, recruit instructors, and create engaging course content in data science and data engineering. They will also assess course performance and identify curriculum gaps to drive improvements.
The Data Engineer will design, build, and optimize ETL/ELT workflows and develop scalable data pipelines. They will also collaborate with cross-functional teams to deliver impactful data solutions.
Design, implement, and maintain modern data pipelines utilizing cloud technologies. Act as a trusted advisor to customers, providing best practices and methodologies for data engineering solutions.
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