• Collaborate with data scientists, analysts, and other stakeholders to understand data requirements and design data models and schemas that facilitate data analysis and reporting • Design, develop, and maintain scalable and efficient data pipelines and ETL processes to ingest, process, and transform large volumes of data from various sources into usable formats • Build and optimize data storage and processing systems, including data warehouses, data lakes, and big data platforms, using AWS services such as Amazon Redshift, AWS Glue, AWS EMR, AWS S3, and AWS Lambda, to enable efficient data retrieval and analysis • Implement and manage real-time data streaming architectures using AWS services like Amazon Kinesis or Apache Kafka to enable real-time data processing and analytics • Perform data profiling, data cleansing, and data transformation tasks to prepare data for analysis and reporting • Implement data security and privacy measures to protect sensitive and confidential data using AWS security services and features • Design and implement data architectures following Data Mesh principles within the AWS environment, including domain-oriented data ownership, self-serve data infrastructure, and federated data governance • Provide technical guidance and mentorship to junior data engineers, reviewing their work and ensuring adherence to best practices and standards

• Collaborate with the Product Owner and team leads to define and design efficient pipelines and data schemas • Build and maintain infrastructure using Terraform for cloud platforms • Design and implement large-scale cloud data infrastructure, self-service tooling, and microservices • Work with large datasets to optimize performance and ensure seamless data integration • Develop and maintain squad-specific data architectures and pipelines following ETL and Data Lake principles • Discover, analyze, and organize disparate data sources into clean, understandable schemas

• Design and evolve scalable, cloud-native data architectures for advanced analytics and AI • Develop and maintain real-time and batch data processing platforms • Define and implement data modeling standards for structured/unstructured data • Integrate innovative technologies (vector databases, LLMs, real-time streaming) • Ensure data quality, lineage, and governance • Collaborate with engineering/product teams to translate business needs into solutions • Optimize platform scalability, cost, and performance in cloud environments • Establish architectural standards for data-driven decision-making

• Define event schemas, required fields, and compatibility rules in collaboration with the CDP team • Implement automated validation and contract enforcement to prevent breaking schema changes • Maintain versioning and compatibility guarantees for event producers and downstream consumers • Build and maintain pipelines that ingest, validate, and process high-volume product events • Ensure event streams are deduplicated, ordered correctly, and safe for downstream consumption • Partner with platform teams to ensure ingestion pipelines scale with product growth • Define and maintain identity stitching logic across anonymous and authenticated users • Handle identity merges, splits, and corrections while preserving tenant boundaries • Ensure identity resolution remains explainable, deterministic, and safe for downstream datasets • Design workflows that allow event datasets and identity graphs to be replayed or rebuilt safely • Build tooling for historical corrections, schema evolution, and dataset reprocessing • Ensure downstream models can be rebuilt without manual intervention when definitions evolve • Provide guidance and tooling that help product teams emit events consistently • Maintain validation checks and schema enforcement that catch instrumentation issues early • Collaborate with engineering teams to evolve instrumentation safely over time • Ensure deletion and suppression requests propagate correctly through event and identity pipelines • Partner with governance and security teams to support policy requirements • Define requirements and interfaces for event infrastructure and downstream analytical systems • Work with platform teams to ensure pipelines remain reliable, scalable, and observable.