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

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.