Analyze the business requirements and needs of the organization.
Architect and design robust, scalable, and secure enterprise data platform leveraging Databricks and various cloud services on Microsoft Azure.
Lead implementation of advanced data analytics solutions utilizing Databricks and Delta Lake architectures to extract, transform, and load (ETL) data.
Leverage Databricks to perform data engineering, artificial intelligence and machine learning tasks.
Collect, process, and analyze large datasets from various sources within the aviation domain (e.g., flight data, baggage information, weather data, operational logs etc.).
Perform data quality checks and ensure data accuracy and consistency.
Identify trends, patterns, and anomalies in data to provide valuable insights and support business decision-making.
Contribute to the development of data models and database designs.
Document data sources, data flows, and analytical processes.
Stay up to date with the latest trends and technologies in data analysis and the aviation industry.
Create insightful and interactive data visualizations and reports using tools like Power BI to communicate findings to non-technical audiences.
Define and govern enterprise-wide cloud architecture standards, security protocols, and operational best practices.
Developing and implementing strategies to connect different systems, ensuring they can share data and functionality
Defining and managing API that allow different software applications to communicate with each other
Defining and managing asynchronous data exchange between applications and as a part of data pipelines
Designing real-time streaming pipelines for IoT time-series data
Diving deep into the details to solve complex technical challenges
Ensure the platform is secure and adheres to best practices for data protection and privacy.
Ensuring high data quality and integrity to avoid operational failures and improve system accuracy
Perform regular audits and maintenance of the platform to ensure its continued performance and stability.
Support project management activities, resource monitoring, technical risk identification & mitigations
Mentor and provide technical leadership to platform engineering and data engineering teams.
Ensure continuous platform optimization, cost management, and high availability of critical analytics infrastructure.
Partner with business stakeholders to translate airport operational requirements into technical blueprints and scalable solutions.

Required Qualifications & Experience

Microsoft Certified: Azure Solutions Architect Expert or equivalent advanced architectural certification.
Databricks Certified Data Engineer Professional or similar advanced credential in big data ecosystems.
Proven track record of designing large-scale digital platforms in complex operational environments, preferably within aviation or large infrastructure.

Educational Background

Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related technical discipline.
Advanced certifications in Enterprise Architecture frameworks such as TOGAF are highly preferred.

Years & Type of Experience

15 to 20 years of progressive experience in IT, with at least 5+ years in a senior architectural capacity.
Demonstrated experience leading large-scale data engineering and analytics transformation programs utilizing Microsoft Azure and Databricks.

Domain Expertise

Deep functional knowledge of cloud computing paradigms (IaaS, PaaS, SaaS) and distributed data systems.
Strong understanding of big data ecosystems, data warehousing, and modern data mesh architectures.
Familiarity with the aviation or large-scale infrastructure domain, including passenger processing, non-aero revenue analytics, and airport operations.

Digital & Operational Tools

Cloud Platforms: Advanced proficiency in Microsoft Azure services (Azure Data Factory, Azure Event Hub, Azure Kubernetes Service, Azure IoT Hub etc).
Data & Analytics: Expertise in Databricks, Apache Spark, Delta Lake, and enterprise Business Intelligence platforms.
Proficiency in one programming languages such as JavaScript/Node.js, Java, Python, etc.
Expertise in databases like PostgreSQL, MS SQL, and MongoDB.
Expertise in APIs, ESB (Enterprise Service Bus), and Kafka.
Integration with IoT platforms (Azure IoT Hub), device protocols (MQTT, AMQP, HTTP)
Experience of data analytics, business intelligence, and AI/ML technologies
Use of AI tools for code generation, pipeline automation, testing
Knowledge of Azure DevOps, Docker, Kubernetes, and enterprise source control systems.
Understanding of machine learning fundamentals (supervised, unsupervised, feature engineering)
Experience supporting ML model lifecycle (training data, inference pipelines)
Exposure to LLMs, GenAI pipelines, and vector databases
Knowledge of feature engineering and model-ready datasets
Knowledge of MLOps tools (MLflow, Kubeflow, Airflow, etc.)
Building CI/CD pipelines for data + ML workflows
Experience of Agile methodologies, Jira, and Confluence.
Knowledge of cybersecurity principles and practices.

Leadership Capabilities

Ability to drive technical consensus and influence technology strategy across diverse stakeholder groups and executive leadership.
Strategic thinker with a demonstrated capability to align IT and platform roadmaps with long-term business objectives.
Proven track record of mentoring senior engineers and fostering a culture of technical excellence and innovation.
Capable of managing complex, multi-million dollar technology initiatives and strategic vendor relationships.

Enterprise Data Architect

Key skills

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