Senior Data Quality Engineer

About Yulu

Yulu is India's leading shared micro-mobility platform, revolutionizing urban transportation through smart, sustainable, and electric-first mobility solutions. With a rapidly growing fleet of tech-enabled electric two-wheelers and a robust battery-swapping infrastructure, Yulu makes last-mile commutes not only efficient but also planet-friendly.

Our IoT-driven platform and smart electric vehicles are helping cities reduce traffic congestion and carbon emissions while empowering millions with affordable and reliable transportation.

Backed by industry giants like Bajaj Auto and Magna International, Yulu operates at the intersection of mobility, technology, and sustainability. Our mission is to reduce congestion, cut emissions, and transform how India moves — one ride at a time.

With millions of rides completed, thousands of EVs on the road, and a rapidly expanding footprint, we're not just building EVs — we're building the future of urban mobility in India.

Learn more: www.yulu.bike

Role Summary

We are looking for a Senior Data Quality Engineer to build and own the data quality framework across Yulu's production data platform. The role will be responsible for ensuring that data is accurate, reliable, complete, timely, and trustworthy across the entire data lifecycle.

The role will involve designing reusable data quality checks, building reconciliation mechanisms between source systems and the data lake, owning data catalogue and metadata standards, establishing data contracts, and leading incident resolution for data quality issues. The candidate will work closely with Data Engineering and other teams to establish quality standards, improve data reliability, and drive adoption of best practices across the organisation.

Key Responsibilities

Data Quality

• Design and manage the data quality framework across production data pipelines.
• Build reusable data quality checks using tools such as Great Expectations or equivalent frameworks.
• Define checks for schema, null values, uniqueness, referential integrity, data volume, freshness, late-arriving data, and business rules.
• Integrate data quality checks at ingestion, transformation, and pre-publish stages.
• Define appropriate actions for quality failures, including blocking pipelines, quarantining data, or raising alerts.
• Build reconciliation between source systems such as MySQL and PostgreSQL and data lake tables.
• Manage data quality alerts and ensure issues are routed to the appropriate teams.
• Monitor and reduce false-positive alerts.
• Track and report data quality metrics such as pass rate, time to detection, time to resolution, and coverage.

Data Catalogue & Metadata

• Own and maintain the data catalogue, including tooling, adoption, and data quality.
• Maintain table- and column-level metadata such as descriptions, owners, source of truth, refresh frequency, and criticality.
• Build and maintain data lineage from source systems to reports and dashboards.
• Enable teams to understand the impact of schema changes before implementation.
• Automate metadata capture from Glue, pipeline code, and query logs.
• Define and manage the deprecation process for tables and columns.
• Track usage to identify datasets and columns that can be safely retired.
• Ensure new datasets have an owner, definition, and required quality checks before moving to production.

Data Reliability & Collaboration

• Define data contracts with upstream teams and ensure adherence to agreed standards.
• Lead data quality incident resolution, including root-cause analysis, correction, and backfill activities.
• Implement preventive checks to avoid recurring data quality issues.
• Mentor engineers and analysts on data quality practices.
• Review pipeline changes to ensure sufficient data validation and quality coverage.

Qualifications

• 5+ years of experience in Data Engineering, Data Quality, or Data Reliability Engineering.
• Hands-on experience with data quality frameworks such as Great Expectations, Soda, dbt Tests, Deequ, or equivalent tools.
• Strong SQL skills, including window functions, complex joins, and queries on large partitioned datasets.
• Strong Python skills and experience building reusable internal libraries.
• Experience with cloud data lake technologies, including S3, Glue, Athena, Spark on EMR, and Apache Iceberg.
• Experience with Airflow or similar orchestration tools.
• Experience with data catalogue or metadata tools such as DataHub, OpenMetadata, Amundsen, Atlan, or Collibra.
• Experience implementing and driving adoption of data quality and data catalogue standards across teams.
• Strong stakeholder management and ability to influence teams without direct authority.

Good to Have

• Experience with dbt, including tests, documentation, and exposures.
• Experience defining Data SLAs/SLOs and reporting them to non-technical stakeholders.
• Knowledge of statistical methods for anomaly detection beyond basic/static thresholds.

What We Look For

• Strong ownership of data quality and reliability.
• Ability to identify and resolve the root cause of data quality issues.
• Good judgement on when a quality check should block, quarantine, or generate an alert.
• Ability to establish and implement data quality standards across teams.
• Strong problem-solving and stakeholder management skills.
• Ability to drive adoption of data catalogue and quality practices across the organisation.