Senior Airline DataEng Developer

Overview

The Senior Data Engineer will be an integral member of theairline's Data & Analytics engineering function, responsible for designing,building, and maintaining robust data pipelines, analytics platforms, andAI-integrated software solutions. A strong foundation in airline domainknowledge is central to this role — the ability to understand airline businessprocesses, operational data, and commercial workflows is what drives thedevelopment of meaningful, fit-for-purpose software solutions rather than generictechnical outputs. The role requires strong software engineering fundamentalscombined with deep expertise in data engineering, analytics tooling, andapplied AI development. This position suits a technically seasoned developerwho has built scalable data systems, taken end-to-end ownership of analyticsproduct delivery, and written production-grade code that powersbusiness-critical airline operations and commercial processes.

Role & Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines supporting batch, streaming, and near real-time ingestion across core airline operational and commercial data sources, ensuring data is available, consistent, and fit for analytical and AI consumption
• Own the full software development lifecycle for airline data and AI products — from requirements analysis, technical design, and data modelling through to build, testing, deployment, and production support across operational and commercial domains
• Engineer and deliver AI-powered data products that embed predictive models, forecasting engines, anomaly detection, and optimization outputs directly into airline workflows
• Develop Generative AI and Agentic AI software components tailored to airline business challenges — from AI-assisted disruption management and intelligent querying of flight and passenger data, to automation of repetitive operational tasks across network, cargo, and ground operations
• Develop and optimize airline-domain data models, analytical data products, and semantic layers covering key business areas such as passenger revenue, yield, on-time performance, seat utilization, maintenance cost, and customer experience on cloud-native platforms
• Implement and maintain data quality frameworks, validation rules, and pipeline observability tooling suited to airline data characteristics — handling operational data spikes, schedule volatility, and high-frequency telemetry feeds
• Support migration and modernisation initiatives — transitioning legacy airline reporting systems and siloed operational data stores to unified cloud-native analytics and AI platforms
• Participate actively in code reviews, enforce development standards, and contribute to technical documentation and architectural decision records across the airline data and AI engineering team
• Collaborate closely with data scientists, ML engineers, revenue analysts, operations controllers, and business analysts to bridge the gap between airline analytical requirements and production-grade engineering delivery

Required Skills

• 15 to 18 years of total experience in data engineering, with significant depth in designing and delivering data pipelines, transformation logic, and analytics engineering solutions using Python, SQL in production environments
• Mandatory airline or aviation domain experience with strong working knowledge of commercial, operational, and engineering data — sufficient to independently translate business requirements into scalable data solutions  
• Proven experience delivering end-to-end data products — covering pipeline development, data modelling, cloud platform deployment, and production support — in business-critical, regulated environments where reliability and data quality are non-negotiable
• Strong expertise in data architecture fundamentals — including dimensional modelling, star and snowflake schemas, Lakehouse patterns, and cloud-native platform development using AWS, Azure, or Databricks at enterprise scale
• Hands-on experience implementing Change Data Capture patterns, orchestrating workflows using tools such as Apache Airflow or MWAA, and building distributed data processing solutions using Apache Spark or equivalent engines
• Demonstrated experience leading or contributing to platform migration programmes — transitioning legacy data warehouses, on-premise ETL tools, and traditional reporting platforms to modern cloud-native data and analytics ecosystems, including re-engineering of legacy jobs, data models, and reporting artefacts
• Proven ability to apply data engineering discipline to data delivery — unit testing of transformation logic, modular pipeline design, CI/CD for data components, version control, and reusable component development
• Demonstrated ability to work across multiple parallel workstreams and collaborate effectively with data scientists, analytics engineers, and business stakeholders to deliver integrated data and AI solutions
• Strong command of data quality and observability — implementing validation frameworks, anomaly detection within pipelines, and monitoring tooling programmatically as part of the engineering delivery rather than as an afterthought

Must-Have Skills

• Airline Domain Expertise — Hands-on experience delivering data and analytics software solutions within an airline or aviation environment, with deep practical understanding of airline business processes, KPIs, and data across commercial, operational, and engineering functions. Ability to independently interpret airline domain requirements and convert them into well-engineered, fit-for-purpose data and AI software products
• Demonstrated ability to architect and build high-volume, high-reliability ETL/ELT pipelines from heterogeneous airline source systems into Data Warehouses or Lakehouse platforms, with strong command over incremental loading strategies, error handling, and pipeline resilience
• Strong command over dimensional modelling, analytical data product design, and semantic layer development — delivering self-serving, business-consumable data assets that directly support airline commercial and operational decision-making
• Proven experience operationalizing predictive, forecasting, optimization, and anomaly detection models at enterprise scale — building the software layer that takes model outputs from experimentation into reliable, monitored, production-grade airline business applications
• Proven ability to re-engineer legacy data warehouses, ETL jobs, and reporting platforms into modern cloud-native equivalents — with strong delivery discipline across assessment, planning, execution, and cutover phases
• Strong culture of code quality, unit testing of data transformation logic, peer review, CI/CD pipeline integration, and SLA-driven production support for data and AI software components
• Ability to clearly articulate complex data and AI engineering decisions to non-technical airline business stakeholders, and to demonstrate measurable business value through data-backed outcomes