Job Title: Senior Data Analyst – Graph & Data Quality
Location: Hyderabad, India
Experience: 5–8 Years
Company: Anblicks
About Anblicks
Anblicks is a cloud data engineering and analytics company helping enterprises modernize their data ecosystems through Cloud, Data Engineering, AI/ML, and Advanced Analytics solutions. We build scalable, secure, and high-performance data platforms that enable organizations to unlock business value from complex datasets.
We are seeking a highly analytical and detail-oriented Senior Data Analyst – Graph & Data Quality to play a critical role in evaluating, validating, and improving complex data and graph-based systems across enterprise environments.
Role Overview
This role sits at the intersection of analytics, data quality, and data strategy. You will assess new data sources, analyze identity and relationship graphs, evaluate entity resolution outputs, and provide actionable recommendations to improve data accuracy, consolidation, and overall integrity.
This is a senior-level position ideal for analysts who thrive in ambiguous environments, can dig deeply into complex datasets, and communicate insights clearly to both technical and business stakeholders.
Key Responsibilities
1. Data Source Evaluation & Validation
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Evaluate new data sources for quality, completeness, coverage, bias, and alignment with existing datasets.
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Perform deep-dive exploratory analysis to identify inconsistencies, edge cases, and integration risks.
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Establish validation frameworks to assess incoming data reliability.
2. Graph & Relationship Analysis
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Analyze graph-based data models to assess accuracy, consistency, and completeness.
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Evaluate identity graphs and entity resolution outputs for consolidation accuracy.
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Identify opportunities for entity merging, refinement, and relationship optimization.
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Detect anomalies, oddities, and unexpected patterns in graph structures.
3. Data Quality Framework Development
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Develop and maintain metrics to measure graph quality, confidence scores, stability, and drift over time.
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Define standards for data integrity, linkage accuracy, and consolidation performance.
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Create repeatable validation methodologies for graph and relationship datasets.
4. Cross-Functional Collaboration
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Partner with Data Engineering and Data Science teams to translate analytical findings into actionable improvements.
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Provide structured feedback on data models, pipelines, and transformation logic.
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Support root-cause analysis of data quality issues and propose remediation strategies.
5. Communication & Ownership
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Produce clear documentation, dashboards, and executive-ready summaries.
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Present complex findings in a clear and concise manner to technical and non-technical stakeholders.
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Own analytical initiatives from problem definition through delivery and follow-up.
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Drive continuous improvement in data governance and quality monitoring processes.
Required Qualifications
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5+ years of experience in data analysis, analytics, or related roles.
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Strong analytical skills with experience working on large, complex datasets.
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Proven experience evaluating data quality, consistency, and edge cases across multiple data sources.
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Familiarity with graph-based data models, network analysis, or relationship-based datasets.
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Ability to identify anomalies, patterns, and structural inconsistencies in complex outputs.
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Strong SQL proficiency for exploratory and validation analysis.
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Excellent written and verbal communication skills.
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Demonstrated ability to work independently and take ownership of analytical initiatives.
Nice-to-Have Qualifications
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Experience working with identity graphs, entity resolution systems, or network analysis platforms.
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Proficiency in Python for exploratory analysis and validation.
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Experience working with Databricks or large-scale distributed data platforms.
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Familiarity with graph quality metrics, confidence scoring models, or validation methodologies.
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Experience collaborating closely with data engineering teams on model and pipeline enhancements.
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Exposure to enterprise data governance and data platform environments.
What Will Set You Apart
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Strong curiosity and investigative mindset toward data anomalies.
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Experience designing measurable data quality frameworks.
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Ability to balance technical rigor with business impact.
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Experience in data modernization or large-scale enterprise data environments.
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Passion for improving data accuracy, reliability, and trustworthiness across systems.
