Data Operations Analyst

Maleda Tech

1d•$60 - $75•Remote

About The Position

We're looking for a Data Operations Analyst to own lead and account data quality across our sales pipeline. In this role, you'll make sure every prospect entering the pipeline - regardless of source system - is accurate, deduplicated, enriched, and ready for the field before a rep ever sees it. This is a high-impact role that blends technical depth with business judgment, directly shaping how confidently our sales teams can act on the data they're given.

Requirements

5-6+ years of experience in data operations or go-to-market engineering, ideally in a B2B or marketplace environment
Hands-on Salesforce (or comparable CRM) experience, including account structuring, deduplication, and field mapping
Strong data quality instincts, with the ability to navigate complex, imperfect data structures and connect business use cases to data pipelines
Experience designing or refining lead qualification criteria and SLAs
Experience sourcing or evaluating third-party data vendors and enrichment tools
Strong Excel/Google Sheets skills
Strong SQL skills, including the ability to write queries for validation, anomaly investigation, and ad hoc analysis
Clear, proactive communicator comfortable partnering directly with sales and field leadership
Process-oriented mindset with the ability to build and document scalable workflows
Must be authorized to work in the U.S. without sponsorship.

Nice To Haves

Python skills
Experience using modern AI tools to build lightweight internal processes and workflows

Responsibilities

Ensure leads and accounts entering the pipeline are accurate and deduplicated before reaching sales reps, regardless of source system
Set and continuously refine the standards that determine lead quality and routing, whether leads are business-assigned or self-sourced
Own the health of CRM data by cleaning up duplicate or broken records and maintaining sound account structures
Serve as the go-to resource for data-related escalations - missing leads, bad matches, or broken processes - fixing root causes rather than one-off tickets
Evaluate where AI can meaningfully improve data QA, enrichment, or anomaly detection, and hold any deployed solution to a measurable quality bar