Data Analyst – Identity & Graph Analytics

Job Role: Data Analyst – Identity & Graph Analytics Location: Hyderabad, India Experience: 5+ Years About Anblicks Anblicks is a Great Place to Work® Certified™ Data and AI company helping enterprises transform data into actionable insights through modern data platforms, cloud technologies, and advanced analytics. We partner with leading technology providers to deliver innovative, scalable, and data-driven solutions. Role Overview We are looking for a detail-oriented Data Analyst – Identity & Graph Analytics with strong expertise in SQL, Python, data quality, and statistical analysis to support the validation and optimization of large-scale Identity Graph products. In this role, you will analyze complex datasets, evaluate identity resolution results, assess matching accuracy, and identify anomalies across person-level and household-level records. You will collaborate with Product, Engineering, and Data Science teams to improve identity matching quality, validate graph behavior, and drive continuous product enhancements. The ideal candidate combines strong analytical capabilities with hands-on data investigation skills and an interest in identity resolution, entity matching, and graph-based data systems. 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.