• Utilize consulting and technical skills to be able to work in a client-facing project environment independently • Be responsible for your own execution and sometimes lead individual work streams on client engagements as assigned and under supervision of engagement lead • Collaborate with other team members to successfully deliver on projects • Work effectively and directly communicate with both internal and client and/or partner teams • Develop full ownership of your execution on client engagements • Design and implement complex ETL/ELT pipelines with evidence of improved data processing times • Successfully lead small data warehousing projects with measurable performance enhancements under management of an engagement lead • Contribute to real-time data processing solutions and manage streaming data • Implement security and compliance measures for data pipelines • Design and implement version control and branching strategies and integrate them into CI/CD for promoting and testing in higher environments • Hands-on experience working with SAP data at the table level • Strong understanding of SAP data structures and relationships, beyond ETL tooling • Ability to interpret SAP data in the context of underlying business processes

• Build & Operate Large-Scale Feature Pipelines: Design and maintain batch/streaming pipelines (Spark, Flink, Databricks, Airflow) producing ML features for ranking models. • Ensure Point-in-Time Correctness: Develop feature sets that enable unbiased offline training and credible online inference. • Develop Embedding & Content Pipelines: Build scalable workflows for metadata, imagery, and multimodal representations; partner with Science teams to operationalize new models. • Architect Data Foundations: Design Delta/Parquet data models and medallion layers, optimizing storage layout and partitioning for latency and cost. • Real-Time Engineering: Build Kafka-based systems for real-time features and user-activity aggregations, ensuring robust handling of out-of-order events and exactly-once semantics. • Governance & Leadership: Define data quality rules and schema evolution processes while collaborating across ML pods to translate model needs into infrastructure.

• Design, develop, and maintain ETL/ELT data pipelines supporting enterprise data platforms. • Implement and enhance data ingestion, transformation, and integration processes using SQL and modern programming languages. • Design and implement automated data quality and validation checks (e.g., schema integrity, completeness, freshness, volume). • Embed data validation and testing logic directly into data pipelines and CI/CD workflows. • Refactor and modernize existing data pipelines to improve reliability, performance, and maintainability. • Collaborate with technical leads, architects, and engineering teams to define and promote standard data engineering and quality practices. • Support deployment, monitoring, and troubleshooting of data pipelines across development, test, and production environments. • Contribute to technical documentation, runbooks, and reusable components to improve team efficiency and consistency. • Participate in design and code reviews with a focus on sustainability, automation, and operational excellence.

• Design, build, and maintain scalable data pipelines and APIs on Google Cloud Platform • Develop automated workflows and data platforms that support analytics, reporting, and AI/ML use cases • Implement best practices for data security, governance, CI/CD, and automated deployment • Collaborate with data engineers, architects, data scientists, and business stakeholders • Produce high-quality, reusable code and mentor team members on best practices • Support testing, deployment, monitoring, and production troubleshooting