Data Operations Engineer
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Job Description
Data Operations Engineer responsible for onboarding and transforming customer data in SaaS platform. Working with SQL and Python to ensure data quality and reliability.
Responsibilities:
- Lead customer data onboarding, including mapping, cleansing, transforming, and importing data from competitor platforms, spreadsheets, and ad-hoc sources
- Build and maintain repeatable ingestion processes and scripts using Python, SQLAlchemy, and Postgres
- Partner with Customer Success Managers to define data requirements and onboarding timelines
- Translate messy, inconsistent customer data into clean internal schemas with accuracy and consistency
- Maintain a library of reusable migration utilities, validation scripts, and automation tools
- Own internal and external reporting requests requiring SQL or data extraction
- Perform one-time data cleanups, corrections, and backfills directly in the SaaS database
- Investigate data anomalies and support engineering with root-cause analysis
- Improve and maintain ETL pipelines to reduce manual engineering work
- Build lightweight automations to streamline recurring operational workflows
Requirements:
- Strong SQL skills (Postgres preferred)
- Comfort working with large, messy Excel, Google Sheets, and CSV datasets
- Python proficiency (SQLAlchemy strongly preferred)
- Experience designing data transformations, mappings, and validations
- Solid understanding of ETL principles, automation, and scripting
- Ability to interpret data models and navigate relational schemas
- High attention to detail and a strong data quality mindset
- Clear communicator with both technical and non-technical partners
- Experience with Python-based migration or ETL frameworks
- Familiarity with SaaS data structures, multi-tenant databases, or systems like CRM, ATS, or LMS platforms
- Experience building reusable internal tools for data operations
- Exposure to Git and basic DevOps workflows
- Comfort troubleshooting and working in production-like environments
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