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Build and maintain cloud data pipelines using Databricks, PySpark, and Delta Lake to enable analytics and AI workloads for enterprise clients.
Designs and leads enterprise-grade data and AI solutions for Prudential, focusing on cloud-native architectures, Databricks-based lakehouses, ETL/ELT pipelines, and Agentic AI for insurance and corporate functions.
Builds scalable Java/Python microservices on Azure, using AI tools to automate coding, testing, and CI/CD while mentoring peers to boost engineering productivity.
Builds AI-driven, cloud-native enterprise software (Java/Python/Azure) with end-to-end ownership, automating workflows and mentoring teams to boost engineering productivity.
Design and lead enterprise-scale data platforms on Databricks, building data lakes, pipelines, and AI-ready architectures for global clients while mentoring teams.
Lead a team to build cloud-native clinical data platforms and AI solutions that transform raw trial data into trusted, AI-ready assets for faster drug development and smarter decision-making.
Lead a team to design and build scalable data pipelines, lakes, and warehouses for government clients using cloud platforms like AWS and Databricks, ensuring data quality and integration.
Build and maintain data pipelines, optimize SQL/NoSQL databases, and implement visualizations in Power BI or Databricks SQL for a global group.
As part of the growth of our Hybrid Cloud & Data practice (AI, Analytics & Data Services), we are looking for a Senior Data Engineer – Hybrid Cloud & Data to participate in the design, development, and…
Build and maintain Databricks-based data pipelines for a global bank, using PySpark and SQL to ingest and transform financial data into scalable reporting layers.
Build and maintain a modern data platform in Python and PySpark that ingests dozens of sources, transforms data into analytics-ready and AI-ready products, and runs reliably at scale on Delta Lake.
Architect enterprise-grade data and AI solutions for Prudential’s insurance business, designing cloud-native platforms, ETL/ELT pipelines, and Agentic AI systems while ensuring governance and scalability.
Design and maintain data pipelines, warehouses, and lakes for a large bank, modernizing legacy Teradata systems to cloud platforms like AWS, Snowflake, and Databricks while ensuring data accuracy and security.
Design and migrate data pipelines from legacy Ab Initio systems to modern cloud stacks (AWS, Databricks, Spark) to build scalable, secure data warehouses and lakes for a large bank.
Leads architecture and delivery of enterprise-scale GenAI and agentic AI systems on GCP, defining standards, pipelines, and guardrails to power AI-native travel retailing platforms.
Build and maintain scalable data pipelines using Azure Databricks, Azure Data Factory, and SSIS to support analytics and reporting for a Mumbai-based product team.
Design and optimize large-scale PySpark data pipelines for enterprise analytics, handling TB–PB datasets with performance tuning and cloud-native Lakehouse architectures.
Own and manage the firm’s data platform, ensuring performant ingestion, storage, analytics, and AI/GenAI use cases within a governed environment to drive strategic decision-making across EMEA.
Join Ferrovial: Where Innovation Meets Opportunity Are you ready to elevate your career with a global leader in infrastructure solving complex problems and generating a positive outcome on people’s lives? At Ferrovial…
Design and implement cloud-native data platforms (lakehouses, streaming pipelines) using Azure/AWS/GCP to support AI/ML and analytics at scale.
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