Senior Data Engineer: AI-Powered Data Pipelines & MLOps

Starhub Ltd

Singapore

On-site

SGD 90,000 - 130,000

Full time

43 hours ago

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Job summary

Starhub Ltd. seeks a Data Engineer to design, develop, and deploy AI-powered, cloud-based products. You will build scalable data pipelines, ensure data quality, and collaborate with data scientists, infra engineers, and stakeholders to deliver production-grade systems.

Responsibilities include automating DataOps/ML Ops workflows, translating requirements into robust data models, and optimizing storage and queries for performance. Excellent cross‑functional collaboration is essential.

Qualifications

Bachelor’s or Master’s in CS/SE/DS or equivalent.
4+ years in data engineering, analytics, or related AI/ML role.
Proficient in Python for ETL/data engineering and Spark (PySpark) for large-scale pipelines.
Experience with Spark SQL, Redshift, PostgreSQL for data marts and analytics.
Hands‑on with Airflow to orchestrate ETL workflows and GitLab CI/CD or Jenkins for pipeline automation.
Familiar with PostgreSQL/Redshift and NoSQL stores: data modeling, indexing, partitioning, and schema evolution.
Proven ability to implement scalable storage solutions: tables, indexes, partitions, materialized views, columnar encodings.
Skilled in query optimization: execution plans, sort/distribution keys, vacuum maintenance, and cost-optimization strategies.
Experience with cloud platforms (AWS): S3/EMR/Glue, Redshift and containerization (Docker, Kubernetes).
Infrastructure as Code using Terraform or CloudFormation for provisioning and drift detection.
Knowledge of MLOps/LLMOps: auto‑scaling ML systems, model registry management, and CI/CD for model deployment.
Strong problem‑solving, attention to detail, and the ability to collaborate with cross‑functional teams.
Exposure to serverless architectures (AWS Lambda) for event-driven pipelines.
Familiarity with vector databases, data mesh, or lakehouse architectures.
Experience using BI/visualization tools (Tableau, QuickSight, Grafana) for data quality dashboards.
Hands‑on with data quality frameworks (Deequ) or GenAI POCs (RAG pipelines).
Client-facing or stakeholder-management experience in data-driven/AI projects.

Responsibilities

Design, develop, and deploy AI-powered, cloud-based products.
Build scalable data pipelines and ensure data quality.
Collaborate with data scientists, infra engineers, sales specialists, and stakeholders.
Automate DataOps/MLOps/LLMOps workflows with CI/CD.
Translate requirements into normalized data models and schemas.
Optimize storage and tune queries for performance.
Map 4G/5G infrastructure metadata to geospatial context and time-series data.
Consume analytics/ML endpoints and real-time streams with proper API versioning.
Provision AWS resources using IaC and ensure security.
Monitor performance and define SLAs for data freshness and uptime.
Document schemas, ETL procedures, and runbooks; mentor juniors.
Convert requirements into robust, production-grade data architectures.