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.
The role involves migrating data to a unified system for an insurance client and recreating business logic within the new solution. You will collaborate closely with the client to refine requirements, create documentation, and optimize system solutions.
The role involves participating in a data migration project from the Stratio platform to Databricks. The engineer will be responsible for executing the migration independently and autonomously.
Design and maintain scalable ETL pipelines and data ingestion workflows using PySpark and AWS Glue. Orchestrate data workflows with AWS Step Functions and develop serverless functions using AWS Lambda.
Lead the data engineering team to design and maintain a scalable end-to-end data platform and proprietary Carbon Database. Collaborate with cross-functional teams to build data pipelines and AI automations that support sustainability goals.
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Lead the data engineering team in designing and maintaining a scalable end-to-end data platform and proprietary Carbon Database. Collaborate with cross-functional teams to build data pipelines and AI automations that support sustainability goals.
Design and operate scalable batch and streaming data pipelines to power analytics and operational workflows. Develop curated data models and implement quality frameworks to ensure reliable, well-governed data access for internal stakeholders.
Design and build scalable data pipelines, integrations, and architectures to support a modern data ecosystem. Collaborate with data scientists to operationalize machine learning models and implement MLOps standards for production-grade AI initiatives.
Develop and maintain end-to-end data pipelines, Spark-driven workflows, and data APIs to support advanced analytics and automation. Build MCP servers and AI agents to integrate agentic workflows into the data engineering lifecycle.
Drive the architectural direction and technical decisions for BILL's core data platform, focusing on scalable infrastructure for ingestion, storage, and serving. Lead the design of complex data capabilities and mentor senior engineering staff to maintain high technical standards.
Design and build production data pipelines and Lakehouse solutions using the Databricks platform to support AI and digital products. Collaborate with clients and cross-functional teams to develop data strategies and maintain scalable data foundations.
Design and optimize scalable data pipelines and modern data architectures to lead global data integration across products. Act as a technical reference to deliver high-quality ETL/ELT solutions and drive data-driven product evolution.
Design and optimize end-to-end data architectures on Microsoft Fabric and Azure, managing the entire data value chain from ingestion to exposure. Provide technical leadership through mentoring, code reviews, and contributing to pre-sales activities and internal practice growth.
Design, develop, and maintain data pipelines using Azure Data Factory and dbt, implementing transformation processes in Snowflake. Ensure data quality and reliability while collaborating with business and architecture teams to optimize the corporate data platform.
Build and own the infrastructure for reliable, well-governed data powering products and business metrics. Lead the evolution of the data platform toward an AI-first architecture and manage the full data stack from ingestion to delivery.
Lead the technical architecture and extension of the configuration-driven data platform to support product insights and ML/LLM infrastructure. Mentor a team of engineers while managing the full DevOps lifecycle of ingestion, curation, and retention services.
Architect and maintain the data model, pipelines, and access layers to support healthcare operations and analytics. Lead technical engineering best practices and ensure data quality and governance across the platform.
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Design and implement scalable end-to-end data pipelines and architectures using the Azure ecosystem to support AI/ML and enterprise analytics. Provide technical leadership by mentoring junior engineers and collaborating with stakeholders to align data engineering with business goals.
Develop reusable, metadata-driven data pipelines and comprehensive data models using Databricks Lakehouse architecture. Implement robust ETL/ELT solutions and event-driven integrations to evolve the Azure-based Data Analytics Platform.
Lead the technical design and delivery of enterprise-scale cloud-native data platforms and serverless applications on AWS. Drive engineering excellence through technical governance, mentoring multidisciplinary teams, and implementing secure, scalable data pipelines.
Design, build, and maintain scalable data pipelines and analytical data models using a modern data stack. Ensure data accuracy and reliability for reporting and business decisions through proactive monitoring and Level 1 support.
Build and scale a data lakehouse on AWS to support financial analytics, reporting, and AI-driven product features. Develop robust ETL/ELT pipelines and integrate vector databases to enable semantic search and machine learning capabilities.
Build and operate secure, multi-tenant data pipelines and APIs to power AI products for Fortune 500 companies. Ensure data reliability and security through automated testing, monitoring, and compliance with SOC 2 and ISO 27001 standards.
Build and maintain scalable ETL/ELT pipelines and data models to support business analytics and product features. Collaborate with cross-functional teams to ensure data quality, security, and performance optimization across the data ecosystem.
Build and maintain scalable ETL/ELT pipelines and data models to support business analytics and product features. Collaborate with cross-functional teams to ensure data quality, security, and performance optimization across the data ecosystem.
Design and develop solutions for data collection, storage, and modeling to ensure high data quality and accessibility for analysts and scientists. Contribute to Big Data cluster standards, data policies, and maintain technological watch on programming languages.
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Design and implement scalable data warehouse solutions and advanced data pipelines to facilitate a data-driven culture. Collaborate with cross-functional teams to translate business needs into technical requirements and lead code reviews.
Design and implement scalable data warehouse solutions and advanced data pipelines to facilitate a data-driven culture. Collaborate with cross-functional teams to translate business needs into technical requirements and lead code reviews.
Responsible for executing test cases and validating results within SiL and remote environments. The role involves collecting test data, analyzing logs, and preparing structured reports to track deviations.
Design and maintain optimal data pipeline architectures and governance solutions within Palantir Foundry. Collaborate with business leaders to translate information requirements into data-centric enterprise solutions.
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