Senior Data Engineer

Quote.com is seeking a Senior Data Engineer to own and enhance the data infrastructure supporting marketing performance, revenue reporting, and business intelligence. The role builds and operates reliable pipelines and conversion feedback systems using Python, SQL, dbt, BigQuery, and Google Cloud Platform, while partnering with Marketing, Finance, Data Analytics, and Partner Management.

Responsibilities:

• Design, maintain, and optimize production data pipelines that ingest partner revenue, advertising costs, and site activity into BigQuery
• Develop and maintain Python-based ingestion services using Cloud Run, Cloud Functions, and third-party integrations, including REST APIs, SFTP, webhooks, and scheduled reports
• Own the dbt transformation layer, developing reliable revenue, lead, and marketing funnel models with appropriate testing and documentation
• Maintain integrations with Google Ads, Meta, and Microsoft Ads to deliver offline conversion and revenue data for campaign optimization
• Manage advertising cost and performance data from Google Ads, Funnel.io, Google Search Console, and related platforms
• Collaborate with Finance and Analytics teams to support revenue reconciliation, month-end close, executive reporting, and Looker dashboards
• Monitor pipeline health, troubleshoot failures, investigate data discrepancies, and improve alerting and observability
• Maintain responsible handling of personally identifiable information (PII), including privacy-related data requests, secure access, and credential management
• Modernize existing infrastructure by consolidating legacy services, improving CI/CD workflows, and bringing configuration under version control
• Contribute to technical planning, documentation, and continuous improvements to data reliability and operational efficiency

Requirements:

• Hands-on expertise with:
• Languages: Python and SQL
• Data Engineering: Building and maintaining production ETL/ELT pipelines, data ingestion, transformation, and reconciliation
• Data Warehousing: BigQuery or comparable cloud data warehouses
• Transformation Tools: dbt, including model development, testing, documentation, and scheduling
• Cloud Infrastructure: Google Cloud Platform, including Cloud Run, Cloud Functions, Cloud Scheduler, Pub/Sub, Secret Manager, and IAM
• Integrations: REST APIs, SFTP, CSV/Excel data feeds, webhooks, and third-party data sources
• Version Control: Git and modern CI/CD workflows, including GitHub Actions or equivalent
• Data Quality: Deduplication, reconciliation, attribution, timezone handling, and management of late-arriving or corrected data
• You should have experience owning and supporting production data systems in a Data Engineering , Analytics Engineering , or Cloud Data Engineering capacity
• 4+ years of professional experience building and operating production data pipelines using Python and SQL
• Strong understanding of data architecture, cloud-based data platforms, and scalable ETL/ELT processes
• Demonstrated experience independently owning production systems, including troubleshooting, root-cause analysis, and incident resolution
• Strong attention to data accuracy, reliability, and consistency across multiple sources and reporting systems
• Excellent problem-solving skills and the ability to diagnose complex issues across interconnected systems
• Effective communicator who can collaborate with technical and non-technical stakeholders across Marketing, Finance, and Engineering
• Comfortable balancing planned engineering initiatives with ongoing operational responsibilities and stakeholder requests
• Passionate about automation, documentation, observability, and continuous improvement
• Marketing Platforms: Google Ads API, Google Data Manager API, Meta Conversions API, Microsoft Ads
• Marketing Analytics: Performance marketing, conversion attribution, GCLID/GBRAID tracking, and offline conversion matching
• Business Intelligence: Looker, LookML, or comparable BI and reporting platforms
• Data Integrations: Funnel.io and Google Search Console
• Industry Experience: Insurance, lead generation, affiliate marketing, or performance marketing
• Data Privacy: PII handling, CCPA/GDPR-related data requests, and hashed-identifier processing
• Infrastructure: Experience migrating legacy services, modernizing cloud infrastructure, and improving deployment automation
• Tools: JIRA / Atlassian Suite, Slack
• Experience or familiarity with any of the following: