Assistant Manager, Data Quality & Analytics

About the team you are joining The Assistant Manager – Data Quality Analytics will support the Turbulence Aware NextGen platform migration by contributing to data quality governance and control, migration readiness, acceptance testing coordination, business analysis, and data science activities. What your day would be like The role will support the Assistant Director, Turbulence Aware NextGen, in designing and validating the Data Quality Control Module of TA NextGen. The role will also help ensure that migration activities, comprehensive testing, data quality assurance, and new analytics features are delivered with accuracy, reliability, and full requirements traceability. The role will apply analytical and data science techniques, including statistical profiling, anomaly detection, data validation, and Python-based analysis, to support the monitoring and continuous improvement of data quality across the platform. Key Responsibilities 1. Data Quality Governance, Assurance Control Support the definition and documentation of data quality requirements, including applicable standards, validation rules, business rules, thresholds, and acceptance criteria, to help ensure the reliable and continuous operation of the Turbulence Aware NextGen platform. Support the Assistant Director in designing and validating the Data Quality Control Module of TA NextGen. Support the design of the Quality Control Module, including quality KPIs, dashboards, alerts, and data science-based controls for: - Airline-provided EDR and ACARS data - Third-party supplemental data - Output datasets distributed to airline participants Support issue resolution with airlines, developers, infrastructure teams, vendors, and internal stakeholders. 2. Data Science Support for Data Quality Control Support the use of data science techniques to improve data quality assurance and control across the TA NextGen platform. Analyze historical and near-real-time datasets to identify patterns, gaps, inconsistencies, latency issues, and abnormal reporting behavior. Apply statistical profiling, trend analysis, anomaly detection, and data validation techniques to identify data quality issues and monitor data reliability with automated or semi-automated data quality checks using SQL, Python, or similar tools. Prepare analysis outputs, notebooks, reports, or dashboards to explain data quality findings to technical and non-technical stakeholders. 3. Data Migration Preparation Reconciliation Support pre-migration data profiling and baseline validation activities. Assist in developing reconciliation frameworks for legacy vs. new platform outputs. Use SQL, Python, or analytical tools to compare datasets, identify discrepancies, and support reconciliation evidence. Track and document discrepancies, resolution actions, and sign-off criteria. Support cutover readiness assessments and post-migration stabilization reviews. 4. Support for Turbulence Aware Platform Migration and New Feature Delivery Support the development and validation of new revenue-generating features. Assist in defining acceptance criteria and validation metrics for nowcast, forecast, and warning products. Support testing activities by preparing, documenting, and maintaining test scripts, and help track test execution progress. Help verify that deployed features meet defined quality, latency, and reliability standards. Assist in preparing documentation and dashboards for customers' onboarding and commercialization. 5. Documentation Governance Maintain comprehensive documentation, including: Data dictionaries Validation rules Business rules Data science assumptions and analytical methods Test plans and scripts Reconciliation reports Analytics feature specifications Data quality dashboards and KPI definitions