Data Quality Analyst

Saicon; Pasadena (CA); On-site; USD 55,000 - 96,000; Full time

Job summary

Saicon seeks a contract data analyst to support data quality, coverage, and cross-source comparison across enterprise datasets. The role focuses on evaluating data consistency, identifying gaps, and translating business rules into reliable facts from imperfect source data.

The ideal candidate will be strong in SQL and Databricks, comfortable with ambiguity, and proactive in engaging domain experts to validate assumptions and findings.

Qualifications

• 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.

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.

Skills

SQL

Databricks

Data quality

Data profiling

ETL concepts

Tools

BI tools

Python

Job description

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.

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

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
• 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 What Success Looks Like
• Clear assessment of data quality and coverage across key source datasets
• Actionable recommendations on which data sources and mappings are fit for operational use
• Improved confidence in derived facts across inconsistent enterprise data
• Well-documented findings, business rules, and validation methodology