Data Quality Engineer
About Us
At Campaignswell, we're transforming marketing analytics with our AI-powered predictive platform used by apps, games, and e-commerce brands worldwide. We deliver highly accurate lifetime value (LTV) predictions within hours and unify product, marketing, and monetization analytics in a single platform.
Founded in 2023 and headquartered in San Francisco, our team is building the future of data-driven growth. As we scale, data quality and reliability are becoming foundational pillars of our product and that’s where you come in.
Responsibilities
- Own and improve data quality across multiple pipelines, datasets, and integrations.
- Design and maintain automated data validation frameworks (freshness, completeness, schema checks, anomaly detection).
- Build monitoring and alerting systems to ensure reliability of ELT/ETL pipelines.
- Investigate data inconsistencies and work cross-functionally to resolve root causes.
- Develop and maintain data SLAs, incident response playbooks, and documentation.
- Partner with Data Engineering, Analytics, and Customer Success teams to ensure data used by clients is always accurate, timely, and trustworthy.
- Improve internal tools and workflows related to data ingestion, observability, lineage, and testing.
- Contribute to continuous improvements of our data platform and operational excellence.
Requirements
- 3+ years of experience in Data Quality, Data Ops, Analytics Engineering, or Data Engineering.
- Solid SQL and Python skills.
- Experience implementing data testing frameworks (e.g., dbt tests, Great Expectations, Soda, or custom tooling).
- Strong understanding of ETL/ELT pipelines and data warehousing concepts.
- Hands-on experience with orchestration tools (Airflow or equivalents).
- Experience with AWS cloud services (S3, Lambda, ECS, etc.).
- Understanding of schema design, data modeling, and data lineage.
- Strong analytical mindset and exceptional attention to detail.
- Excellent written and verbal communication skills.
Nice to Have
- Experience with Snowflake and/or ClickHouse.
- Knowledge of monitoring/observability tools (e.g., Prometheus, Grafana, OpenTelemetry).
- Familiarity with event-based architectures and webhook ingestion.
- Experience supporting ML pipelines from a data reliability standpoint.
