About this role
Build and scale reliable ETL/ELT pipelines that unify data from systems like Zuora, RevenueCat, and Salesforce. Monitor and validate data quality to support business reporting, dashboarding, campaign tracking, and lifecycle reporting across cybersecurity brands.
Key Responsibilities
- Build and maintain ETL/ELT pipelines using APIs and SFTP
- Extract and transform Salesforce data for B2B funnel reporting and attribution
- Monitor pipelines, troubleshoot issues, and resolve errors
- Validate data and investigate discrepancies across sources and pipelines
- Create clean datasets for dashboards, campaign tracking, and lifecycle reporting
Technical Overview
You will develop backend data integrations and operational flows using Python, SQL, APIs, and SFTP, running pipelines on AWS services including S3, Glue, Redshift, Athena, and Lambda. Responsibilities include Salesforce data extraction for B2B funnel reporting and attribution, plus end-to-end data validation and documentation of ETL logic and data models.
Ideal Candidate
The ideal candidate is a data engineer with 2+ years of experience building and operating ETL/ELT pipelines, with strong Python and SQL skills. They have hands-on experience integrating Salesforce data via APIs and batch ingestion, using AWS services like S3, Glue, Redshift, Athena, and Lambda, and they can validate data quality using B2B and SaaS metrics for reporting and attribution.
Must-Have Skills
2+ years of experience in data processing or data engineeringPythonSQLExperience working with Salesforce data via APIs and batch ingestion pipelinesExperience with AWS data services (S3GlueRedshiftAthenaLambda)Understanding of B2B and SaaS metrics and how they are used in reportingStrong communication skillsability to work with business stakeholdersdata integrationETL/ELT pipelinesdata validation
Nice-to-Have Skills
Experience working with ETL tools like Talend or similarExperience optimizing batch pipelines (full refresh to incremental)Familiarity with Redshift-based transformations (stored proceduresstaging layers)Exposure to ad platformsaffiliateor partner dataExperience supporting dashboarding for marketing or sales teamsInterest in marketing analyticsattributionor lifecycle reportingExposure to BI tools (LookerQuickSight)Experience validating end-to-end data quality across multiple systems
Tools & Platforms
ZuoraRevenueCatSalesforceSFTP (Secure File Transfer Protocol)TalendAmazon S3 (Simple Storage Service)AWS GlueAmazon RedshiftAmazon AthenaAWS LambdaLookerAmazon QuickSight
Required Skills
ETL/ELT pipelinesAPIsSFTPSalesforceZuoraRevenueCatPythonSQLAWS (S3GlueRedshiftAthenaLambda)data validationdata modelspipeline monitoringMRRfunnel stagesLTVchurnLookerQuickSightdashboardingdata warehouse hygiene
Hard Skills
ETL/ELT pipelinesAPIsSFTPthird-party integrationsSalesforce data via APIsbatch ingestion pipelinesdata integrationdata quality checksdata validationdata modelingETL logicdata warehouse hygieneSQLPythonAWS data services (S3GlueRedshiftAthenaLambda)MRR (monthly recurring revenue)funnel stagesLTV (lifetime value)churnB2B and SaaS metricsdashboard datasetscampaign trackinglifecycle reportingpipeline monitoringtroubleshooting pipeline issueserror resolutiondocumentation of ETL logic and data modelsstored proceduresstaging layersfull refresh to incrementalRedshift-based transformationsBI tools (LookerQuickSight)
Soft Skills
strong communication skillsability to work with business stakeholderswork with business reporting teamstroubleshooting documentation clarity
Keywords for Your Resume
Data Engineerdata processingdata engineeringETL/ELT pipelinesExtract and transformAPIsSFTPSecure File Transfer ProtocolSalesforceZuoraRevenueCatpipeline monitoringtroubleshootdocument ETL logicdata modelsdata validationdata warehouse hygienePythonSQLAWS data servicesS3Amazon S3GlueAWS GlueRedshiftAmazon RedshiftAthenaAmazon AthenaLambdaAWS LambdaMRRmonthly recurring revenuefunnel stagesLTVlifetime valuechurnB2B metricsSaaS metricsTalendLookerQuickSightAmazon QuickSight
Deal Breakers
Must have 2+ years of experience in data processing or data engineering, Must have strong Python and SQL skills, Must have AWS data services experience (S3, Glue, Redshift, Athena, Lambda)
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