Senior Data Engineer – Engineering
Summary
Builds and maintains scalable data pipelines, warehouses, and APIs to extract, transform, and integrate data across systems, ensuring accuracy and compliance while collaborating with cross-functional teams in an Agile environment.
- Design, develop and deploy data tables, views and marts in data warehouses, operational data store, data lake and data virtualization
- Perform data extraction, cleaning, transformation, and flow
- Design, build, launch and maintain efficient and reliable large-scale batch and real-time data pipelines with data processing frameworks
- Integrate and collate data silos in a manner which is both scalable and compliant
- Collaborate with Project Manager, Data Architect, Business Analysts, Frontend Developers, Designers and Data Analyst to build scalable data-driven products
- Be responsible for developing backend APIs & working on databases to support the applications
- Work in an Agile Environment that practices Continuous Integration and Delivery
- Work closely with fellow developers through pair programming and code review process
Requirements
- 2-3 years of relevant working experience as a data engineer
- Proficient in general data cleaning and transformation (e.g. SQL, pandas, R, etc) to ensure data accuracy and consistency
- Proficient in building ETL pipeline (eg. SQL Server Integration Services (SSIS), AWS Database Migration Services (DMS), Python, AWS Lambda, ECS Container task, Eventbridge, AWS Glue, Spring)
- Proficient in database design and various databases (e.g. SQL, PostgreSQL, AWS S3, Athena, mongodb, postgres/gis, mysql, sqlite, voltdb, cassandra, etc)
- Experience in cloud technologies such as GPC, GCC (i.e. AWS, Azure, Google Cloud)
- Experience and passion for data engineering in a big data environment using Cloud platforms such as GPC, GCC (i.e. AWS, Azure, Google Cloud)
- Knowledge about system design, data structure and algorithms
- Familiar with data modelling, data access, and data storage infrastructure like Data Mart, Data Lake, Data Virtualisation and Data Warehouse for efficient storage and retrieval
- Familiar with rest api and web requests/protocols in general
- Familiar with big data frameworks and tools (eg. Hadoop, Spark, Kafka,RabbitMQ)
- Familiar with W3C Document Object Model and customized web scraping (e.g. BeautifulSoup, CasperJS, PhantomJS, Selenium, Nodejs, etc)
- Familiar with data governance policies, access control and security best practices
- Comfortable in at least one scripting language (eg. SQL,Python)
- Comfortable in both windows and linux development environments
Core Competencies
Demonstrates expertise in designing and developing data pipelines, data warehouses, and backend APIs while ensuring data accuracy and compliance. Proficient in cloud technologies and big data frameworks, with a strong focus on data engineering best practices.
Highest-signal resume keywords
- Data Pipeline Development
- ETL Pipeline Building
- Database Design
- Cloud Technologies Experience
- Data Governance Knowledge
ATS Optimization Keywords
Hard Skills
- SQL
- Python
- Data Cleaning
- Data Transformation
- ETL
- AWS
- PostgreSQL
- Hadoop
- Spark
- Data Modeling
Soft Skills
- Collaboration
- Agile Methodology
- Pair Programming
Industry Keywords
- Data Warehouse
- Data Lake
- Data Virtualization
- Big Data
- Data Governance
Tools & Technologies
- AWS Lambda
- SQL Server Integration Services
- AWS Glue
- Eventbridge
- MongoDB
- RabbitMQ
- BeautifulSoup
- Selenium
- Linux
- Windows