Data Analyst – Identity & Graph Analytics

Job Role: Data Analyst – Identity & Graph Analytics

About Anblicks

Role Overview

Key Responsibilities

• Identity Graph Analysis: Analyze identity resolution outputs and client datasets to identify fragmented identities, duplicate or anomalous person records, and incorrect household groupings.
• Match Quality Assessment: Investigate false positives, false negatives, and matching inconsistencies to evaluate identity resolution accuracy.
• Data Validation & Testing: Validate graph matching behavior across real-world scenarios, business rules, and identity use cases.
• SQL & Python Analytics: Use SQL and Python to query, transform, analyze, and validate large, complex datasets.
• Benchmarking & Test Data: Create, maintain, and enhance datasets for graph testing, quality validation, performance benchmarking, and regression analysis.
• Exploratory Data Analysis: Perform deep-dive and ad hoc analyses to identify patterns, anomalies, trends, and opportunities for improving graph quality.
• Cross-Functional Collaboration: Work closely with Product, Engineering, and Data Science teams to investigate issues and recommend improvements to matching accuracy and graph performance.
• Statistical Measurement: Support the evaluation of match quality using metrics such as precision, recall, false-positive rate, and false-negative rate.
• Reporting & Insights: Develop and maintain reports, dashboards, and documentation highlighting graph quality, validation results, performance trends, and improvement opportunities.
• Continuous Improvement: Support testing backlogs, validation workflows, quality monitoring, and research initiatives in collaboration with the Lead Data Scientist.

Required Skills & Qualifications

• Bachelor's degree in Data Science, Analytics, Statistics, Mathematics, Computer Science, or a related field.
• 5+ years of experience in data analytics, data quality, data validation, or a related analytical role.
• Strong proficiency in SQL, including complex queries, joins, aggregations, data profiling, and analysis of large datasets.
• Hands-on experience with Python or R for data analysis, data manipulation, and statistical evaluation.
• Strong understanding of data quality assessment, data validation techniques, and exploratory data analysis.
• Experience identifying duplicates, inconsistencies, anomalies, and patterns across complex datasets.
• Knowledge of statistical analysis and data-driven problem-solving techniques.
• Ability to translate analytical findings into actionable insights and communicate results clearly to technical and business stakeholders.
• Strong attention to detail, critical thinking, and a structured approach to investigating data issues.

Preferred Qualifications

• Experience in identity resolution, entity matching, record linkage, or customer identity analytics.
• Familiarity with identity graphs, graph-based systems, relationship mapping, or large-scale data processing platforms.
• Understanding of person-level identity, householding, customer profiles, and identity matching logic.
• Experience designing test datasets, benchmark datasets, and regression testing scenarios for data-intensive products.
• Practical knowledge of match-quality metrics, including precision, recall, F1-score, and false-positive/false-negative analysis.
• Exposure to Customer Data Platforms (CDPs), MarTech, AdTech, or identity intelligence solutions.
• Experience working with cloud-based data platforms such as Snowflake, Databricks, or equivalent technologies.

What Makes You a Good Fit?

• You enjoy investigating complex datasets and uncovering hidden patterns and inconsistencies.
• You can combine SQL, Python, and statistical analysis to evaluate data quality and matching accuracy.
• You are comfortable working with large-scale data and translating findings into practical recommendations.
• You collaborate effectively with technical teams to solve data quality challenges and improve product performance.