Data Engineer - Data Services
Data Engineer at Mox, a Hong Kong digital bank, designing, maintaining, and improving analytical and operational data infrastructure — data lake, pipelines, batch and real-time processing, and metadata/lineage systems. Core stack includes Python, Spark, Airflow, Kafka, Docker/Kubernetes, and AWS services.
- Compensation
- Not disclosed
- Location
- Hong Kong (SAR), Hong Kong (SAR)
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- General computing concepts: Unix environments, networking, distributed and cloud computing
- Python frameworks and tools: pip, pytest, boto3, pyspark, pylint, pandas, scikit-learn, keras
- Workflow scheduling and monitoring tools: Apache Airflow, Luigi, AWS Batch
- Columnar and big data databases: Athena, Redshift, Vertica, Hive/Hadoop
- Container management and orchestration: Docker, Docker Swarm, ECS, EKS/Kubernetes, Mesos
- CI/CD tools: CircleCI, Jenkins, TravisCI, Spinnaker, AWS CodePipeline
- Distributed messaging and event streaming systems: Kafka, Pulsar, RabbitMQ, Google Pub/Sub
- Streaming data processing frameworks: Spark Streaming, Apache Beam, Apache Flink
- General AWS or cloud services: Glue, EMR, EC2, ELB, EFS, S3, Lambda, API Gateway, IAM, CloudWatch
- Version control: git commands, branching strategies, collaboration etiquette, documentation best practices
- Agile/Lean project methodologies: Scrum, Kanban
Nice to have
- JVM languages, frameworks and tools: Kotlin, Java, Scala / Maven, Spring, Lombok, Spark, JDK Mission Control
- RDBMS and NoSQL databases: MySQL, PostgreSQL / DynamoDB, Redis, HBase
- Enterprise BI tools: Tableau, Qlik, Looker, Superset, PowerBI, Quicksight
- Data science environments: AWS SageMaker, Project Jupyter, Databricks
- Log ingestion and monitoring: ELK stack (Elasticsearch, Logstash, Kibana), Datadog, Prometheus, Grafana
- Metadata catalogue and lineage systems: Amundsen, Databook, Apache Atlas, Alation, uMetric
- Data privacy and security tools and concepts: Tokenization, Hashing and encryption algorithms, Apache Ranger
Full description
**About Mox
**Mox is built by and for the ones who aspire to live life to the fullest – we call them Generation Mox!
The name Mox reflects the endless opportunities we can create, - Mobile eXperience; Money eXperience; Money X (multiplier), eXponential growth, eXploration… it’s all up for us to define together.
**Why Mox
**Mox helps you grow – your money, your world, your possibilities. We equip you with the financial management tools, information and insights you need to make your dreams, big or small, come true.
Everything at Mox – from our products, features, to rewards – is designed based on customer research, tailor made for your needs. We care about what customers care about, especially in data security and privacy. Data ethics is core to everyone here at Mox.
Mox rewards you with an array of banking and lifestyle benefits. Who says banking can’t be fun?
**What we are looking for?
As a Data Engineer you'd be working with us to design, maintain, and improve various analytical and operational services and infrastructure which are critical for many other functions within the organization. These include the data lake, operational databases, data pipelines, large-scale batch and real-time data processing systems, a metadata and lineage repository, which all work in concert to provide the company with accurate, timely, and actionable metrics and insights to grow and improve our business using data. You may be collaborating with our data science team to design and implement processes to structure our data schemas and design data models, working with our product teams to integrate new data sources, or pairing with other data engineers to bring to fruition cutting-edge technologies in the data space.
Highly relevant: (ideally familiar with at least one of the technologies in most of the below categories)
- General computing concepts and expertise: Unix environments, networking, distributed and cloud computing
- Python frameworks and tools: pip, pytest, boto3, pyspark, pylint, pandas, scikit-learn, keras
- Workflow scheduling and monitoring tools: Apache Airflow, Luigi, AWS Batch
- Columnar and big data databases: Athena, Redshift, Vertica, Hive/Hadoop
- Container management and orchestration: Docker, Docker Swarm, ECS, EKS/Kubernetes, Mesos
- CI / CD tools: CircleCI, Jenkins, TravisCI, Spinnaker, AWS CodePipeline
- Distributed messaging and event streaming systems: Kafka, Pulsar, RabbitMQ, Google Pub/Sub
- Streaming data processing frameworks: Spark Streaming, Apache Beam, Apache Flink
- General AWS or Cloud services: Glue, EMR, EC2, ELB, EFS, S3, Lambda, API Gateway, IAM, Cloudwatch