Mid-Level - Data Analyst (W2)

Mid-Level - Data Analyst.
Los Angeles, CA - Hybrid
6-12 months (high possibility of extension)
Need two professional references at the time of submission.
W2

Role Summary
We are seeking a contract data analyst to support complex data quality, coverage, and cross-source comparison work across multiple enterprise datasets. This role will focus on evaluating data consistency, identifying gaps and mismatches across systems, and helping translate business rules into reliable facts from imperfect source data. The ideal candidate is strong in SQL and data analysis, comfortable working through ambiguity, curious about learning the business domain, and proactive about engaging domain experts when needed to validate assumptions or findings.

Required Skills and Experience
• Strong SQL skills and experience analyzing large, messy enterprise datasets
• Experience with data quality assessment, data profiling, and reconciliation across
multiple systems
• Hands-on experience with Databricks
• Working knowledge of basic ETL concepts and data flow validation
• Ability to work with ambiguous source data and infer reliable patterns using
business logic
• Familiarity with graph-style representations of complex data models or ontologies
• Strong analytical thinking with attention to detail and comfort investigating edge
cases
• Curiosity about learning unfamiliar business domains quickly
• Willingness to chase clarity by engaging with domain experts directly rather than
making unsupported assumptions
• Ability to communicate findings clearly to both business and technical audiences

Preferred Qualifications
• Experience working with enterprise data domains such as ERP, HR, CRM, or Billing
• Experience using Python, notebooks, or BI tools for exploratory analysis and
reporting
• Experience documenting business rules, exceptions, and source-system behavior
in a structured way
• Exposure to enterprise platform data is a plus

Key Responsibilities
• Perform data quality, coverage, and comparison analysis across multiple internal
data sources
• Assess consistency, completeness, and reliability of identifiers and cross-system
relationships
• Analyze ambiguous or conflicting source records and help determine fit for
business use cases
• Apply and refine business rules to derive usable facts from noisy, incomplete, or
inconsistent data
• Compare new datasets against existing sources to identify strengths, weaknesses,
and coverage tradeoffs
• Support exploratory analysis in Databricks and contribute to basic ETL-oriented
data investigation workflows
• Produce clear analysis, recommendations, and validation findings for business and
technical stakeholders
• Partner with domain experts and reach out when needed to validate uncertain
outputs or business interpretations
• Document assumptions, logic, data issues, and decision frameworks in a
structured way