Senior Data Engineer

Chicory is seeking a Senior Data Engineer to lead the development and expansion of their data platform. The role involves working with high-volume, real-time ad events, partner APIs, and internal business systems using GCP, BigQuery, Dataflow, Cloud Composer, Python, SQL, and Superset. The Senior Data Engineer will have the autonomy to define and implement the next version of the data lake and warehouse architecture, build pipelines and data models, and improve data access and usage across the company. The position offers broad technical ownership and the opportunity to establish engineering practices that will support Chicory's data systems and future data team.

• Posséder et développer la plateforme de données derrière la publicité, les campagnes, les produits et les rapports financiers.
• Définir la prochaine version de l'architecture de notre lac de données et de notre entrepôt de données et diriger sa mise en œuvre.
• Construire et maintenir des pipelines ETL et ELT batch et streaming avec Python, SQL, Apache Beam et Dataflow.

• Deep hands-on knowledge of BigQuery, including schema and table design, partitioning and clustering, query plans and performance, workload patterns, permissions, retention, and cost management
• Demonstrated success designing, rearchitecting, and operating a production analytical data platform that includes a data lake or durable raw layer, a cloud data warehouse, governed data models, and business-facing data marts
• Hands-on production experience building batch and streaming systems with Apache Beam and Dataflow and with Apache Airflow and Cloud Composer, including retries, idempotency, dependency management, duplicate and late-arriving events, schema evolution, replay, backfills, monitoring, and failure recovery
• Demonstrated ability to operate as an autonomous senior technical owner: assess an unfamiliar environment, make architectural decisions, sequence migrations, resolve ambiguity, lead incidents, and communicate tradeoffs with both engineers and non-technical stakeholders
• Experience with software-engineering practices for data systems, including Git, automated testing, CI/CD, code review, infrastructure as code such as Terraform, and clear technical documentation
• Applied understanding of data governance, least-privilege access, privacy and sensitive-data handling, auditability, retention, and the controls required for financial or revenue-impacting data
• Strong Python and advanced SQL skills, including the ability to write maintainable, tested production code and review work across ingestion, transformation, orchestration, and data-serving layers
• Track record of establishing automated data quality, reconciliation, observability, lineage, alerting, service-level objectives, incident response, and operational runbooks for critical data products
• Strong data-modeling judgment across dimensional, event, and domain models, with experience creating stable facts, dimensions, slowly changing dimensions, semantic definitions, and reusable analytical datasets
• Experience enabling BI and self-service analytics through Superset or a comparable platform, including curated datasets, governed metrics, access patterns, dashboard performance, and stakeholder education