• Design, build, and maintain event streaming pipelines that ingest data from client systems, internal services, and third-party sources into the data platform • Develop and operate analytical databases and data models optimized for high-volume event data queries and low-latency access • Write production Elixir and Python services for event processing, transformation, and routing • Integrate legacy event pipelines with modern streaming infrastructure, designing migration paths that minimize risk and disruption to downstream consumers • Build and maintain monitoring, alerting, and observability tooling for event data systems, ensuring pipeline health, data freshness, and SLA compliance • Define and enforce event schemas, data contracts, and quality standards in partnership with producing and consuming teams • Collaborate with the data platform, product engineering, and analytics teams to understand data needs and deliver reliable event data products • Participate in system design reviews and help establish best practices for the Events Data team

• Support the design and delivery of data ingestion pipeline and infrastructure • Assist in the successful migration of legacy data lifecycle management to new platform without disrupting existing data consumers • Establish and maintain SLIs and SLOs for new ingestion and data platform with dashboards and alerting to track performance • Build flexible data storage layer supporting a variety of use cases, e.g., transactional, analytic, and machine learning workloads • Implement comprehensive monitoring, observability, and incident response practices for all event data pipelines and services • Collaborate with product engineering, analytics, and machine learning teams to define contracts, functional requirements, and standards • Design and implement a semantic metadata layer that classifies and labels data assets across both products, enabling consistent data discovery, exposure policies, and identification of cross-product data reuse opportunities • Architect and deliver a multi-tenant data model that supports secure data sharing and isolation across clients, with controls designed to meet regulatory and government compliance requirements

• Lead the full lifecycle of data engineering projects, from initial requirement gathering with stakeholders to production deployment and monitoring. • Design, develop and maintain complex data transformations, ensuring high data quality and performance using scripting languages like Python, Airflow, DBT and databases such as Snowflake or similar Data Lakes. • Build, scale, and maintain automated workflows using Apache Airflow to manage sophisticated data dependencies. • Maintain high engineering standards through CI/CD implementation and rigorous version control using GitHub. • Implement automated processes for data validation, ensuring high standards of data quality, accuracy, and integrity across all pipelines. • Act as a technical partner to the Analytics, Sales, and Marketing teams, building curated datasets that drive strategic decision-making.

• Design and implement our BigQuery environment • Set up dbt as the home for our transformation logic • Introduce dimensional models for core business entities • Configure native Firestore-to-BigQuery integration • Work with source-system owners, the analyst team, and engineering teams