Enterprise Data Architect

Enterprise Data Architect

Responsibilities

• 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.

Qualifications

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.