Data Engineer – Spark Developer

at  AVN DIGITAL PTE LTD

Avensys is a reputed global IT professional services company headquartered in Singapore. Our service spectrum includes enterprise solution consulting, business intelligence, business process automation and managed services. Given our decade of success we have evolved to become one of the top trusted providers in Singapore and service a client base across banking and financial services, insurance, information technology, healthcare, retail, and supply chain.
We are currently looking to hire Data Engineer – Spark Developer
This is an exciting opportunity to expand your skill set, achieve job satisfaction and work-life balance. More details as below.
Job Type: One-year Renewable Contract

REQUIREMENTS :-

• Degree in computer science or a numerate subject (e.g. engineering, sciences, or mathematics) or Bachelor’s/ Master degree with 6 years of experience
• Hand-on Development experience with Spark, ScalaSpark and Distributed computing.
• Development and implementation experience of applications for 4-6 years.
• 4 to 6 years’ experience designing and developing in Python.
• 4 to 6 years’ experience in Hadoop Platform (Hive, HDFS and Spark)
• 3 to 5 years’ experience with Unix shell scripting
• 3 to 5 years’ experience with SQL
• 2 to 3 years’ experience with Spark programming.
• Knowledge of micro-services architecture and cloud will be added advantage.
• Knowledge of Java and Scala will be added advantage

• Creating complex, enterprise-transforming applications on diverse, high energy teams
• Working with the latest tools and techniques
• Hands-on coding, usually in a pair programming environment
• Working in highly collaborative teams and building quality code
• The candidate must exhibit a good understanding of model implementation, data structures, data manipulation, distributed processing, application development, and automation.
• The candidate must have a good understanding of consumer financial products, data systems and data environments, and processes that are necessary for the implementation of Risk and Finance models