4-6 years of Data Engineering experience with proven hands-on experience in Big Data technologies and Business Intelligence tools.
Understanding of Agile or other rapid application development methods. Exposure to design and coding across one or more platforms and languages as appropriate.
Exposure to application design, software development, and automated testing methodologies.
Experience with CI/CD practices using Git, Jenkins, and code integration/review processes.
Backend platform: Experience building data connectors with relational & nonrelational backend databases including MongoDB, PostgreSQL, and custom API Connectors.
Google Cloud Platform Expertise: Hands-on experience with GCP services like BigQuery, Dataflow, Cloud Storage, Pub/Sub, Cloud Composer (Airflow DAGs), and Cloud Functions.
Programming Languages: Strong proficiency in Python and SQL for data ingestion, processing, and manipulation.
ETL/ELT: Experience with ETL/ELT processes and data warehousing concepts.
Data Modeling: Understanding of data modeling principles and techniques.
Cloud Technologies: Knowledge of cloud computing concepts and standard processes.
AI / ML: Experience with Statistical / Predictive Modeling, Machine Learning, A/B testing, and designing feature experiments
AI Frameworks:
Experience supporting AI integrations ML pipelines including BigQuery ML, or other GCP AI/ML services
BI Tools: Experience working with BI tools including Tableau and PowerBI
Problem-Solving: Excellent analytical and problem-solving skills.
Experience working with large datasets, ODL, identifying ingestion issues and proactively collaborating to resolve data related issues.
Passion for travel/airports a plus.
Experience with LLMs, Generative AI, or NLP systems.
Familiarity with feature stores and model registries.
Knowledge of data governance and compliance standards.
Experience with real-time analytics and streaming architectures.
Certifications in cloud or AI technologies.
Design, build, and maintain scalable data pipelines (batch and streaming) supporting real-time / near-real-time data integration for Lounge Services Analytics use cases.
Develop and optimize ETL/ELT processes for structured and unstructured data from various backend DBs and sources including MongoDB, PostgreSQL, APIs, and third-party systems.
Architect and manage cloud-native data infrastructure/warehouses within Google Cloud Platform (BigQuery) environments.
Orchestrate Data Transformations with Cloud Composer / Airflow DAGs.
Enable trusted, analytics-ready datasets to support Tableau dashboards and analytics.
Experience with observability and monitoring tools such as Datadog, CloudWatch, Prometheus, or similar platforms.
Implement data monitoring, logging, and observability frameworks to ensure data quality, integrity, governance and security standards are met.
Build AI / ML models and integrations to support predictive & personalized use cases including lounge recommendations, capacity forecasting, and lounge optimization
Collaborate with cross-functional teams including Analytics, Product & Business to define data requirements and deliver solutions.
Optimize data storage and processing for performance and cost-efficiency (partitioning and indexing solutions).
Implement and enforce data governance standards, including data classification, access controls, and retention policies.
Develop and maintain metadata management practices, including data lineage, catalog documentation, and business glossary alignment.