• 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.

• Develop reusable, metadata-driven data pipelines with strong focus on database design and SQL optimization • Work extensively with AWS-based datasets and data infrastructure including Redshift, RDS, S3, Glue, Athena, and DMS • Support comprehensive ETL development and optimization using advanced SQL techniques and query performance tuning • Automate and optimize data platform processes leveraging AWS data services ecosystem • Build robust integrations with data sources and consumers using AWS native solutions • Proactively resolve performance and data quality issues through SQL optimization and database tuning • Leverage DBT for data transformation modeling and Jenkins/GitHub Actions for CI/CD automation • Contribute to platform documentation and runbooks with focus on database best practices • Propose and implement improvements to data platform architecture using AWS data services