{"id":3475795,"url":"https://alion.io/job/zynga-data-quality-engineer","title":"Data Quality Engineer","company":{"id":1549,"name":"Zynga","domain":"zynga.com","url":"https://alion.io/company/zynga","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":91,"open_postings":10,"ghost_share":0,"stale_share":0.3,"repost_share":0.1,"time_to_fill_p50_days":51,"computed_at":"2026-11-01T06:10:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"junior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":13000,"max_usd":30000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":12},"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"dbt","optional":false},{"name":"SQL","optional":false},{"name":"A/B Testing","optional":true},{"name":"AWS","optional":true},{"name":"Databricks","optional":true},{"name":"GDPR","optional":true},{"name":"Great Expectations","optional":true},{"name":"Snowflake","optional":true}],"status":"live","first_seen_at":"2026-10-29T18:35:00Z","employer_posted_date":null,"last_verified_at":"2026-10-29T18:35:00Z","board_verified":false,"closed_at":null,"days_open":3,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":3},"description":"Responsibilities: \nDevelop and maintain advanced automated data quality checks for game telemetry, player events, and backend systems.\nMonitor complex data pipelines for anomalies, schema changes, and integrity issues, ensuring high availability and reliability.\nDefine, document, and enforce global data quality standards, validation rules, and SLAs across the data ecosystem.\nLead collaboration with engineering teams to ensure accurate and scalable instrumentation of in-game events.\nInvestigate and resolve complex data discrepancies, performing deep root-cause analysis to prevent recurrence.\nBuild and oversee dashboards and alerting systems to surface data quality issues in real time for stakeholders.\nPartner with analytics and product teams to ensure data sets are optimized and fit for strategic decision-making.\nMap and document data definitions, lineage, and quality expectations to improve organizational data literacy.\nSupport technical compliance efforts related to player data privacy.\n\nWho We Think Will Be A Great Fit: \nProblem Solving and Analytical Thinking: Ability to diagnose complex technical issues within large-scale data environments and implement systemic solutions.\nAttention to Detail: A rigorous approach to data accuracy and the ability to identify subtle patterns or anomalies in high-volume datasets.\nCross-Functional Collaboration: Proven ability to work effectively with both technical engineering teams and non-technical product stakeholders.\nCommunication: Skill in translating complex data quality issues into actionable insights and clear technical documentation.\nProactivity: A self-driven mindset focused on identifying potential risks and improving observability before issues impact the business.\n\nRequirements: \nBachelor's degree in Computer Science, Data Engineering, or a related technical field (or equivalent experience).\nExtensive experience in data engineering, data quality, or analytics engineering, typically requiring 5+ years of relevant experience.\nAdvanced SQL proficiency and expertise in managing and querying large, complex.\nHands-on experience with data pipeline and orchestration tools (e. g., Airflow, dbt, Spark).\nDeep understanding of data validation frameworks and automated testing\nProven experience working with high-volume, event-based data such as telemetry, logs, and user activity streams.\n\nPreferred Qualifications: \nExperience within the gaming industry or similar high-concurrency, real-time data.\nMastery of specialized data quality tools (e. g., Great Expectations, Monte Carlo).\nProficiency with cloud platforms and data warehouses (e. g., AWS, Databricks, Snowflake).\nFamiliarity with A/B testing frameworks and the nuances of experimentation.\nKnowledge of global data privacy regulations (e. g., GDPR, CCPA) and best practices for player data governance.\nSkills\nAWS, Airflow, Databricks, SQL, Snowflake, Spark, 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