• Design, build, and maintain data pipelines and ingestion frameworks on AWS using services such as AWS Glue, S3, and Redshift. • Implement and maintain S3‑based data lakes, including raw, staged, and curated layers, following agreed standards and best practices. • Set up and optimize Amazon Redshift environments, including schema design, distribution and sort key strategies, and performance considerations. • Develop reliable ingestion patterns for file‑based and database sources, including SFTP‑to‑S3 workflows. • Implement secure secrets management using AWS Secrets Manager, ensuring credentials are never stored in code or configuration files. • Build reusable, maintainable transformation logic aligned with agreed modelling and semantic‑layer principles. • Monitor, troubleshoot, and improve pipeline reliability, data quality, and operational stability. • Collaborate with architects, analysts, and client stakeholders to translate requirements into scalable data solutions. • Contribute to defining and enforcing data engineering standards, security practices, and governance patterns. • Support clients during platform adoption, knowledge transfer, and handover.

• Design, build, and maintain end‑to‑end data pipelines in Azure using Azure Data Factory, Azure Blob Storage / ADLS, and Azure Synapse Analytics. • Implement and maintain Azure‑based data lakes, including raw, staged, and curated layers, following agreed standards and best practices. • Design and optimise Azure Synapse Analytics environments, including table structures, distribution strategies, partitioning, and performance tuning. • Develop ingestion patterns for data sourced from on‑premises SQL Server, Azure SQL Database, other relational databases, and Cosmos DB. • Build secure and scalable orchestration workflows using Azure Data Factory for batch‑oriented data processing. • Implement secure authentication and access control using Azure Active Directory, ensuring proper role‑based access and least‑privilege principles. • Develop reusable, maintainable transformation logic aligned with agreed data modelling and semantic‑layer standards. • Support analytical workloads through integration with Azure Analysis Services and downstream consumption in Power BI. • Monitor, troubleshoot, and improve pipeline reliability, data quality, and platform performance. • Collaborate with architects, analysts, and client stakeholders to translate business requirements into scalable Azure data solutions. • Contribute to defining and enforcing data engineering standards, security practices, and governance patterns. • Support clients during platform adoption, knowledge transfer, and handover.

• Develop and automate large scale, high-performance batch and streaming data processing systems to power Airbnb’s Guest and Host products, machine learning models, and business insights. • Partner closely with infra teams to improve scalability, data governance, and efficiency • Evangelize high quality software engineering practices towards building data infrastructure and pipelines at scale, collaborate with infrastructure teams to streamline best practices. • Advocate for high bar for data and engineering quality ensure eng deliverables are reliable, efficient, well documented, testable, & maintainable. • Design our data models for optimal storage and understanding, with thoughtful dataflows to power critical product and business requirements.

• Design and implement data architecture and ETL solutions. • Collaborate with business stakeholders, data engineers, and data scientists to understand data requirements and design scalable and flexible data models. • Develop data ingestion pipelines and frameworks to efficiently capture and integrate data from various sources into data warehouse platform (on-prem & cloud). • Define data storage and retrieval mechanisms, including multi-tenant data partitioning, indexing, and compression, to optimize performance and cost efficiency. • Create and maintain data pipelines and ETL/ELT processes to transform, cleanse, and integrate data across different data sources. • Perform data profiling, analysis, and troubleshooting to identify and resolve data-related issues. • Identify technical areas for improvement and present detailed business cases for improvements or new areas of opportunities. • Work with external and internal teams to resolve infrastructure-related issues when developing integration solutions. • Work with IT functional leads to understand the application architecture and setup right integration mechanisms. • Follow DevOps practices across all analytics systems. • Communicate complex ideas in simple terms, and effectively interact with decision makers.