• Lead, design, and scale data solutions to support Journey Analytics initiatives, focusing on code quality, reusability, and reliable data platforms. • Set the technical direction, overseeing the evolution of data architectures. • Lead a team of data engineers to deliver high-quality, performant datasets for analytics and reporting use cases. • Mentor a team of data engineers, fostering best practices in coding, architecture, and data engineering standards. • Drive the technical strategy for Journey Analytics data platforms, ensuring scalability, maintainability, and performance. • Oversee maintenance, optimization, and automation of code repositories in GitHub. • Guide the refactoring of legacy codebases to improve maintainability, scalability, and reusability. • Drive the design and implementation of modular, reusable data components. • Oversee development and management of automated data pipelines in Databricks. • Ensure data quality, governance, performance, and reliability across all data pipelines and datasets.

• Lead, design, and scale data solutions to support Journey Analytics initiatives, with a strong focus on code quality, reusability, and reliable data platforms. • Set the technical direction, oversee the evolution of data architectures, and lead a team of data engineers to deliver high-quality, performant datasets for analytics and reporting use cases. • Mentor a team of data engineers, fostering best practices in coding, architecture, and data engineering standards. • Define and drive the technical strategy for Journey Analytics data platforms, ensuring scalability, maintainability, and performance. • Oversee maintenance, optimization, and automation of code repositories in GitHub, ensuring high-quality and consistent development practices. • Guide the refactoring of legacy codebases to improve maintainability, scalability, and reusability across multiple use cases. • Drive the design and implementation of modular, reusable data components to support multiple journeys and reduce duplication. • Oversee development and management of automated data pipelines in Databricks, ensuring reliability and scalability for downstream consumption. • Establish and enforce standards for scalable data modeling to support current and future analytics use cases. • Ensure data quality, governance, performance, and reliability across all data pipelines and datasets. • Partner with analytics, product, and engineering stakeholders to align data solutions with business needs and priorities. • Proactively identify risks, bottlenecks, and improvement opportunities, and drive mitigation strategies at a team and platform level. • Promote continuous improvement of data processes, documentation, and engineering practices.

Job Title: Data Engineer (PySpark / Databricks) Experience: 5–9 Years Location: Pune (Hybrid – Capco Office) Job Summary We are looking for a skilled Data Engineer with strong expertise in PySpark, Databricks, and modern data engineering practices. The ideal candidate will have hands-on experience in building scalable data pipelines, working with large datasets, and leveraging cloud-based data platforms. Key Responsibilities Design, develop, and maintain scalable ETL/ELT data pipelines Work extensively with PySpark and Apache Spark for large-scale data processing Build and manage workflows using Apache Airflow Develop and optimize data solutions on Databricks (Jobs, Delta Lake) Work with cloud-based data lakes (S3 or equivalent) Write efficient and complex SQL queries for data transformation and analysis Run and manage Spark workloads on EMR Serverless or other managed Spark platforms Ensure data quality, reliability, and performance optimization of pipelines Must Have Skills Strong hands-on experience with PySpark and Apache Spark internals Experience with Databricks (Jobs, Delta Lake) Proficiency in Apache Airflow for workflow orchestration Solid experience building ETL/ELT pipelines at scale Strong SQL skills and experience with Data Warehouse (DWH) systems Experience running Spark workloads on EMR Serverless or managed Spark platforms Hands-on experience with cloud data lakes (S3 or equivalent) Good to Have Skills Experience with Delta Lake / Apache Iceberg Exposure to streaming frameworks (Spark Structured Streaming, Kafka) Familiarity with CI/CD pipelines for data engineering workflows Knowledge of data governance, cataloging, and lineage tools

• Projetar, desenvolver e manter pipelines de dados e transformações no Palantir Foundry (PySpark, SQL, Code Workbook). • Construir e gerenciar ontologias, tipos de objetos e relacionamentos dentro da camada de Ontologia do Foundry. • Implementar e suportar fluxos de trabalho e agentes de IA/ML usando Palantir AIP. • Colaborar com partes interessadas do negócio para traduzir requisitos em soluções de dados escaláveis. • Garantir a qualidade dos dados, governança e melhores práticas de segurança em todos os pipelines. • Criar e manter dashboards e aplicações de dados dentro do Foundry Workshop. • Fornecer orientações técnicas e documentação para decisões de arquitetura de dados.