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Senior Data Engineer (PySpark, ML Software Engineering)

Keyrus

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Job Summary


Job Type
Permanent

Seniority
Senior (≥ 6 yrs)

Years of Experience
At least 5 years

Tech Stacks
ETL
Docker
Analytics
EMR
pySpark
Databricks
UNIX
Spark
NoSQL
Kubernetes
SQL
Python

Job Description


As part of Keyrus’ solution delivery, we are also in a position to recruit and place technical consultants to complement on existing client projects with their expertise. As such, we seek innovative and agile people to support ambitious and forthcoming technological challenges.

The team in Singapore is currently looking for a Senior Data Engineer with strong expertise in handling PySpark pipelines, in order to support our client activities.

The high level job scope and skills required are:

Help our team to connect to and from the Data Lake environment, take over Python based data pipelines, work with team to find and data prep for analytics. Productionize models with Data Scientists.
Prioritized skill keywords: Python, PySpark, SKLearn, ML Software Engineering, SQL, Unix

Main responsibilities

  • Work closely with Data Scientists on feature engineering and productionizing models to be robust and scalable
  • Focus on solutions that provide data analytics for omnichannel/marketing/sales domains
  • Identify data engineering needs for advanced analytics teams
  • Support the Data and Platforms Group to build foundational infrastructure, data governance, and master data management in support of analytics services
  • Use expertise to improve the quality of data models, data flows and data processing of data systems, reporting, analytics solutions

Qualifications and Profile

Mandatory Skills:

Overall 4/5+ years of experience in Information technology with following skills,
  • 4+ Data Engineering Experience
  • Strong knowledge in Machine Learning tools such as SKLearn, Spark, Databricks, Docker, EMR, Kubernetes
  • Knowledge of Data Management Platforms (SQL, NoSQL, Cloud, ETL, Datawarehousing)
  • Knowledge of MLOps and productionization and scaling of models and managing the ML ecosystem

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