Data Engineer, Data Analytics

2 weeks ago


Singapore Income Insurance Limited Full time

Responsibilities:

  • Take the ownership of key data pipelines in AI /ML use cases, from design and implementation to optimization and maintenance.
  • Serve as a technical specialist within a data science team. Design, develop, and operate data pipelines to provide highquality datasets for analytical and AI / ML use cases.
  • Collaborate with other data engineers, analysts, data scientists, product specialists, and other stakeholders to build wellcrafted, pragmatic, and robust engineering solutions that meet business requirements.
  • Stakeholder management and engagement. Proactively engage with stakeholders to understand their data needs and translate them into technical requirements for data pipelines in AI / ML.
  • Maintain documentation of processes, code changes, and data flows.
  • Demonstrate a strong understanding of data privacy regulations such as PDPA to ensure zero breaches.
  • Manage existing runs and deployment of AI / ML model pipelines to ensure efficient data processing and model training.
  • Foster an innovative and growthoriented mindset, continuously seeking opportunities to enhance data engineering processes and drive improvements across the organization.

Requirements:

  • At least 5 years of experience in data engineering with relevant experience in the big data ecosystem.
  • A bachelor's degree in computer science or equivalent.
  • Proficient in SQL, Python, and Spark. Experience with streaming frameworks like Kafka and Spark Streaming is a plus.
  • Proven experience in implementing MLOps practices on AWS.
  • Experience with Data Warehouses such as Redshift, BigQuery, Snowflake, Hive, and S
  • Experience in leveraging DevOps frameworks to orchestrate and monitor production deployments '" Jenkins, Git/AWS CodeBuild, AWS CodeDeploy.
  • Experience in developing data pipelines leveraging AWS services such as Glue/EMR, Lambda, CloudWatch, and Athena.
  • Intermediatelevel knowledge of Linux commands and shell scripting.
  • Good understanding of the data science model development and deployment process.
  • Proven record of designing and implementing largescale data processing pipelines.
  • Passionate about technology and always looking to upskill based on new developments in data engineering practice.
  • AWS certifications will be a plus.

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