Job Description

Why AirAsia Move

Are you ready to take off and be part of AirAsia Move. AirAsia Move is the latest offering from AirAsia Group, focused on leveraging cutting-edge technologies to build and scale data-driven products and services across the travel, e-commerce, and logistics ecosystems. We are transforming the way people experience travel, offering seamless access to travel-related services. As a part of our expansion, we are looking for a talented Lead Data Engineer to join our growing team and help us unlock the full potential of our data capabilities.

Key Responsibilities:

1. Data Architecture & Pipeline Development:

• Lead the design and implementation of scalable, reliable, and optimized data pipelines that enable seamless data ingestion, transformation, and delivery.

• Architect, build, and manage end-to-end data workflows, ensuring efficient data processing for both real-time and batch systems.

• Work with stakeholders across product, analytics, and engineering teams to ensure data solutions meet business needs.

• Optimize and maintain data pipelines for performance, scalability, and cost-efficiency.

• Lead the design, development, and optimization of MLOps pipelines and infrastructure to streamline the deployment, monitoring, and maintenance of machine learning models at scale.

• Drive the adoption of best practices for continuous integration, continuous deployment (CI/CD), and automated testing in machine learning workflows.

• Define and enforce standards for model versioning, governance, and lifecycle management.

2. Model Deployment & Monitoring:

• Design and implement automated workflows for deploying machine learning models in production environments, ensuring models are delivered on time and meet required performance metrics.

• Implement model monitoring systems to ensure the ongoing health and performance of models in production, including model drift detection, data quality monitoring, and performance alerting.

• Work with data scientists to ensure models are deployable, reproducible, and maintainable in production environments.

• Oversee the operationalization of machine learning models, ensuring scalability, efficiency, and performance of both batch and real-time systems.

3. Cloud Infrastructure & Data Integration:

• Leverage cloud platforms (e.g., Google Cloud) to build and scale data solutions.

• Work with various data integration technologies (e.g., APIs, ETL tools, Kafka, Pub/Sub) to ensure seamless data flow between systems.

• Implement data versioning, lineage, and governance to maintain data integrity, security, and compliance.

4. Data Quality & Monitoring:

• Ensure that data quality, accuracy, and consistency are maintained across all data pipelines and systems.

• Implement and monitor data quality checks, logging, and alerting to ensure early detection of issues in the data pipelines.

• Continuously evaluate and improve data architectures, ensuring data availability and minimizing downtime.

5. Collaboration, Leadership & Team Management:

• Collaborate with cross-functional teams, including data scientists, engineers, product managers, tech and devops to understand business needs and deliver impactful AI/ML solutions.

• Lead a team of data and MLOps engineers, providing mentorship, guidance, and performance feedback to foster a collaborative and innovative team culture.

• Promote best practices in data engineering, code quality, testing, and deployment.

• Work with senior leadership and other departments to define and prioritize data-related initiatives and align with business objectives.

• Lead the development of technical roadmaps and strategies for long-term data infrastructure goals.

• Document processes, workflows, and best practices for knowledge sharing across teams.

7. Innovation & Continuous Improvement:

• Stay up to date with the latest trends, tools, and technologies in the data engineering space.

• Propose innovative solutions to improve the speed, quality, and scalability of data systems.

• Drive a culture of continuous improvement by proactively identifying areas for optimization and automation.

Required Qualifications:

• Education & Experience:

  • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.

  • 6+ years of hands-on experience in data engineering, including at least 3 years in a leadership or management role.

  • Proven experience in building large-scale, cloud-based data infrastructure and data pipelines for high-volume, high-velocity data.

  • Strong experience with ETL tools, data orchestration frameworks (e.g., Apache Airflow), and batch/streaming data processing (e.g., Apache Kafka, Spark).

  • Expertise in working with cloud platforms such as Google Cloud Platform (GCP)

• Technical Skills:

  • Proficiency in Python, Java, or Scala; experience with SQL and NoSQL databases (e.g., BigQuery, MongoDB, PostgreSQL).

  • Deep understanding of data warehousing concepts and experience in designing and managing data models.

  • Proficiency in GCP Services such as Vertex AI, Kubeflow, TensorFlow Extended (TFX), Google Kubernetes Engine (GKE),BigQuery ML, Cloud Functions & Cloud Run, BigQuery, Cloud Storage, Composer, Cloud Pub/Sub & Dataflow, AI Building Blocks, Model Monitoring & Explainability

  • Experience with data governance, data quality, and data privacy best practices.

  • Familiarity with containerization technologies (Docker, Kubernetes) and infrastructure-as-code (Terraform, CloudFormation).

• Soft Skills:

  • Strong leadership, communication, and collaboration skills, with experience managing cross-functional teams.

  • Excellent problem-solving and analytical abilities, with a focus on scalability and efficiency.

  • Ability to translate complex technical concepts into business value for non-technical stakeholders.

  • Passionate about mentoring and developing teams to reach their potential.