Quality Engineer - Analytics

Proto Labs; Hyderabad; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Proto Labs is seeking an Analytics Engineer (Quality) to design and evolve data quality frameworks across our modern data platform in India. You will collaborate with Analytics Engineers, Data Engineers, and Data Analysts to ensure trust, reliability, and accuracy of data products.

You will build intelligent testing, monitoring, and anomaly detection, plus alerting and incident management processes to protect critical KPIs.

Qualifications

• 4+ years in Analytics Engineering, Data Analytics, or related data-focused role.
• Strong SQL skills and experience with analytical datasets and data models.
• Analytical mindset with ability to identify patterns, anomalies, and data quality risks.
• Understanding of KPIs and business outcomes related to data quality.
• Experience embedding automated testing, validation, and quality controls into CI/CD workflows.
• Experience with data quality testing, monitoring, and source freshness checks.

Responsibilities

• Build, evolve, and maintain data quality frameworks across the data platform.
• Develop automated data quality tests, validation controls, monitoring, and source freshness checks.
• Establish and propagate data quality standards across the organization.
• Embed data quality controls throughout the data development lifecycle to prevent issues in production.

Skills

SQL

Data Quality

Analytics Engineering

CI/CD

dbt

SQLMesh

Cloud Data Platforms

Tools

dbt

SQLMesh

Job description

Join the team as our new Analytics Engineer (Quality) – India

You will be responsible for building and evolving the frameworks that ensure trust, reliability, and accuracy across our modern data platform. Working closely with Analytics Engineers, Data Engineers, and Data Analysts, you will design intelligent data quality, testing, monitoring, and alerting frameworks that protect critical business processes and KPIs while enabling scalable and reliable data products.

What You'll Do:

Build & Evolve Data Quality Frameworks

• Design, implement, and continuously improve data quality frameworks across the data platform.
• Build and maintain automated data quality tests, validation controls, monitoring frameworks, and source freshness checks throughout the data lifecycle.
• Establish quality standards and practices that can be consistently adopted across the organisation.
• Embed data quality controls throughout the data development lifecycle to improve reliability before issues reach production.

Develop Intelligent Testing, Monitoring & Anomaly Detection

• Develop statistical checks and anomaly detection techniques to identify unexpected changes in data and business metrics.
• Design intelligent testing approaches that balance rigorous quality standards with pragmatic operational practices.
• Identify and distinguish between minor data inconsistencies and data quality incidents requiring immediate action.
• Continuously improve monitoring approaches to maximise data reliability while minimising unnecessary alerts and alert fatigue.

Design Alerting & Incident Management Processes

• Design alerting and notification frameworks that prioritise data quality issues based on business impact.
• Establish incident management processes for responding to critical data quality issues.
• Investigate data quality incidents and perform end-to-end root cause analysis across ingestion, transformation, and reporting layers.
• Translate technical data quality issues into clear business impact and actionable improvements.

Partner Across the Data & Analytics Organisation

• Work closely with Analytics Engineers, Data Engineers, Data Analysts, and other stakeholders to improve the quality and reliability of data products.
• Collaborate with cross-functional teams to understand business processes, KPIs, and the impact of data quality issues on business outcomes.
• Support teams in identifying quality risks across complex data pipelines and systems.
• Drive consistent adoption of data quality standards, automation, testing, and continuous validation practices.

Enable Data Quality Visibility & Continuous Improvement

• Create and maintain reporting and dashboards that provide visibility into data quality health across the platform.
• Track and communicate test coverage, data quality incidents, alert volumes, and quality trends.
• Use data quality insights to identify recurring issues, improvement opportunities, and areas of operational risk.
• Guide and train teams on data quality frameworks, testing strategies, observability practices, and reliability standards.

What It Takes:

Technical

• 4+ years of experience in Analytics Engineering, Data Analytics, Data Quality, or a related data-focused role.
• Strong SQL skills and experience working with analytical datasets and data models.
• Strong analytical and statistical mindset, with the ability to identify patterns, anomalies, and data quality risks.
• Strong understanding of business processes, KPIs, and the impact of data quality issues on business outcomes.
• Experience designing and implementing data quality controls, monitoring frameworks, validation processes, and source freshness checks.
• Experience embedding automated testing, validation, and quality controls into CI/CD and development workflows.
• Experience performing root cause analysis across complex data pipelines and systems.
• Hands-on experience with dbt, SQLMesh, or similar SQL-based transformation tools.
• Experience working with modern data platforms and cloud data warehouses is preferred.

Leadership & Collaboration

• Experience working closely with Analytics Engineers, Data Engineers, Data Analysts, and cross-functional stakeholders.
• Strong communication and stakeholder management skills, with the ability to translate technical data quality issues into business impact.
• Ability to influence teams and drive consistent adoption of data quality standards and best practices.
• Strong problem-solving skills with a structured and pragmatic approach to investigating data quality issues.
• Ability to balance technical quality requirements with operational priorities and business impact.

Mindset

• Quality-focused mindset with a strong commitment to data reliability, accuracy, and trust.
• Pragmatic approach to problem solving, recognising that not every data issue carries the same level of business impact.
• Analytical and curious approach to identifying patterns, anomalies, and underlying causes.
• Continuous improvement mindset with a focus on automation, scalability, and operational excellence.
• Strong sense of ownership for the reliability and trustworthiness of data products.
• Collaborative mindset with a passion for enabling teams to adopt better data quality and reliability practices.