Data Engineer - Data Services

Data Engineer - Data Services Data Engineer - Data Services Technology-CIOHong Kong (SAR)Permanent -; Hong Kong; On-site; HKD 420,000 - 640,000; Full time

Job summary

Mox is seeking a Data Engineer to design, maintain, and improve analytical and operational services and infrastructure, including the data lake, data pipelines, and large-scale batch and real-time processing systems. You will work with the data science team to structure data schemas and models, and collaborate with product teams to integrate new sources.

You will be part of a data-driven organization focused on secure, scalable data infrastructure, leveraging cloud services and modern

Qualifications

• Design, maintain and improve analytical and operational services and infrastructure.

Responsibilities

• Design, maintain and improve data infrastructure used across the company.
• Develop data pipelines for batch and real-time processing.
• Collaborate with data science to structure data schemas and models, and integrate new sources.

Skills

Unix environments

Python

Workflow orchestration

Big data concepts

AWS basics

Agile/Lean

Tools

Apache Airflow

Luigi

AWS Batch

Kafka

Pulsar

RabbitMQ

Google Pub/Sub

Docker

Kubernetes

Hive/Hadoop

Spark

Sagemaker

Databricks

Job 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 e

Xperience; Money e

Xperience; Money X (multiplier), e

Xponential growth, e

Xploration 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 make 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

• 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
• Distributed messaging and event streaming systems: Kafka, Pulsar, RabbitMQ, Google Pub/Sub
• General AWS or Cloud services: Glue, EMR, EC2, ELB, EFS, S3, Lambda, API Gateway, IAM, Cloudwatch
• Agile/Lean project methodologies and rituals: Scrum, Kanban

Also good 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
• 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

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