{"id":2776611,"url":"https://alion.io/job/yulu-senior-data-quality-engineer","title":"Senior Data Quality Engineer","company":{"id":4444088,"name":"Yulu","domain":"yulu.bikerole","url":"https://alion.io/company/yulu-2","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":43000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":59},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Iceberg","optional":false},{"name":"Collibra","optional":false},{"name":"dbt","optional":false},{"name":"Great Expectations","optional":false},{"name":"MySQL","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-09-28T18:03:32Z","employer_posted_date":null,"last_verified_at":"2026-09-28T18:03:32Z","board_verified":false,"closed_at":null,"days_open":20,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":20},"description":"About Yulu\nYulu 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.\nOur IoT-driven platform and smart electric vehicles are helping cities reduce traffic congestion and carbon emissions while empowering millions with affordable and reliable transportation.\nBacked 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.\nWith 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.\n Learn more: www.yulu.bike\n\nRole Summary\nWe 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.\nThe 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.\n\nKey Responsibilities\nData Quality\nDesign and manage the data quality framework across production data pipelines.\nBuild reusable data quality checks using tools such as Great Expectations or equivalent frameworks.\nDefine checks for schema, null values, uniqueness, referential integrity, data volume, freshness, late-arriving data, and business rules.\nIntegrate data quality checks at ingestion, transformation, and pre-publish stages.\nDefine appropriate actions for quality failures, including blocking pipelines, quarantining data, or raising alerts.\nBuild reconciliation between source systems such as MySQL and PostgreSQL and data lake tables.\nManage data quality alerts and ensure issues are routed to the appropriate teams.\nMonitor and reduce false-positive alerts.\nTrack and report data quality metrics such as pass rate, time to detection, time to resolution, and coverage. \n\nData Catalogue & Metadata\nOwn and maintain the data catalogue, including tooling, adoption, and data quality.\nMaintain table- and column-level metadata such as descriptions, owners, source of truth, refresh frequency, and criticality.\nBuild and maintain data lineage from source systems to reports and dashboards.\nEnable teams to understand the impact of schema changes before implementation.\nAutomate metadata capture from Glue, pipeline code, and query logs.\nDefine and manage the deprecation process for tables and columns.\nTrack usage to identify datasets and columns that can be safely retired.\nEnsure new datasets have an owner, definition, and required quality checks before moving to production. \n\nData Reliability & Collaboration\nDefine data contracts with upstream teams and ensure adherence to agreed standards.\nLead data quality incident resolution, including root-cause analysis, correction, and backfill activities.\nImplement preventive checks to avoid recurring data quality issues.\nMentor engineers and analysts on data quality practices.\nReview pipeline changes to ensure sufficient data validation and quality coverage. \n\nQualifications\n5+ years of experience in Data Engineering, Data Quality, or Data Reliability Engineering.\nHands-on experience with data quality frameworks such as Great Expectations, Soda, dbt Tests, Deequ, or equivalent tools.\nStrong SQL skills, including window functions, complex joins, and queries on large partitioned datasets.\nStrong Python skills and experience building reusable internal libraries.\nExperience with cloud data lake technologies, including S3, Glue, Athena, Spark on EMR, and Apache Iceberg.\nExperience with Airflow or similar orchestration tools.\nExperience with data catalogue or metadata tools such as DataHub, OpenMetadata, Amundsen, Atlan, or Collibra.\nExperience implementing and driving adoption of data quality and data catalogue standards across teams.\nStrong stakeholder management and ability to influence teams without direct authority. \n\nGood to Have\nExperience with dbt, including tests, documentation, and exposures.\nExperience defining Data SLAs/SLOs and reporting them to non-technical stakeholders.\nKnowledge of statistical methods for anomaly detection beyond basic/static thresholds. \n\nWhat We Look For\nStrong ownership of data quality and reliability.\nAbility to identify and resolve the root cause of data quality issues.\nGood judgement on when a quality check should block, quarantine, or generate an alert.\nAbility to establish and implement data quality standards across teams.\nStrong problem-solving and stakeholder management skills.\nAbility to drive adoption of data catalogue and quality practices across the 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