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 high-quality datasets for analytical and AI / ML use cases.
- Collaborate with other data engineers, analysts, data scientists, product specialists, and other stakeholders to build well-crafted, 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 growth-oriented 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 S3.
- 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.
- Intermediate-level knowledge of Linux commands and shell scripting.
- Good understanding of the data science model development and deployment process.
- Proven record of designing and implementing large-scale 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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