Data Quality Engineer

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

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

Flexible schedule and remote work

Participation in interesting and large-scale projects

Friendly and professional team atmosphere

Competitive salary