Data Operations Engineer
About The Position
People Architects is proud to partner with our client, a high growth-stage SaaS company to recruit a Data Operations Engineer- a critical, hands-on role that sits at the intersection of data, engineering, and operations. This is not a traditional data analyst role, and it is not a large-scale platform data engineering position. It is a high-ownership, engineering-adjacent role for someone who enjoys taking messy, real-world data and turning it into clean, reliable, production-ready systems that teams can trust. As the Data Operations Engineer, you will own customer data onboarding and operational data workflows end-to-end, allowing the core engineering team to stay focused on product development. In this role you’ll work across SQL, Python, spreadsheets, CSVs, and ETL tooling to ingest, transform, validate, and maintain customer data inside a SaaS platform. If you enjoy solving data puzzles, building leverage through automation, and being the person who quietly makes everything work better, this role is built for you.
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
Nice To Haves
- 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
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
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
