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Salary: USD 160,000 - 185,000 / annual
Chicory is a contextual advertising platform for CPG and grocery that connects consumers with brands and retailers at high-intent moments—from recipe discovery to checkout. The platform reaches 150 million grocery shoppers monthly across 7,000+ websites and apps, powering 70+ leading retailers.
As Senior Data Engineer, you will own and expand the data platform behind Chicory's advertising, campaign, product, and financial reporting systems. You'll work with high-volume, real-time ad events, partner APIs, files, and internal business systems using GCP, BigQuery, Dataflow, Cloud Composer, Python, SQL, and Superset. The role spans the full data stack—from ingestion and orchestration through warehouse design, data marts, and BI—giving you visibility into how data flows from the advertising platform into company-wide reports.
You will have autonomy to define and implement the next version of Chicory's data lake and warehouse architecture while remaining hands-on. Key responsibilities include: mapping ad requests, bids, impressions, clicks, campaign metadata, spend, revenue, and product events into BigQuery; designing the new BigQuery structure (raw, staging, core, and reporting datasets); building and maintaining batch and streaming ETL/ELT pipelines with Python, SQL, Apache Beam, and Dataflow; implementing Cloud Composer and Apache Airflow DAGs with clear dependencies and alerts; establishing a version-controlled SQL transformation workflow using Dataform or dbt; building data marts for campaign delivery, spend/revenue reconciliation, publisher payments, and product analytics; investigating and correcting data mismatches across source systems; implementing data quality tests and alerts; maintaining Superset datasets and shared metrics; optimizing BigQuery usage and cost; managing data access with GCP IAM; and using Git, automated tests, code review, and infrastructure-as-code practices.
You will work closely with backend and cloud engineers, Campaign Management, Finance, Product, and leadership. Your work directly supports campaign performance, revenue reporting, publisher payments, and product decisions. When pipelines fail or reports look wrong, you will lead investigation and recovery, leaving runbooks, diagrams, and documentation for the team.
Qualifications:
- Demonstrated success designing, rearchitecting, and operating a production analytical data platform with a data lake or durable raw layer, cloud data warehouse, governed data models, and business-facing data marts
- 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
- Strong Python and advanced SQL skills; ability to write maintainable, tested production code and review work across ingestion, transformation, orchestration, and data-serving layers
- 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
- Strong data-modeling judgment across dimensional, event, and domain models; experience creating stable facts, dimensions, slowly changing dimensions, semantic definitions, and reusable analytical datasets
- Track record of establishing automated data quality, reconciliation, observability, lineage, alerting, SLOs, incident response, and operational runbooks for critical data products
- Experience enabling BI and self-service analytics through Superset or comparable platform, including curated datasets, governed metrics, access patterns, dashboard performance, and stakeholder education
- Applied understanding of data governance, least-privilege access, privacy and sensitive-data handling, auditability, retention, and controls required for financial or revenue-impacting data
- Experience with software-engineering practices for data systems, including Git, automated testing, CI/CD, code review, infrastructure as code (Terraform), and clear technical documentation
- Demonstrated ability to operate as an autonomous senior technical owner: assess unfamiliar environments, make architectural decisions, sequence migrations, resolve ambiguity, lead incidents, and communicate tradeoffs with engineers and non-technical stakeholders
