Cloud-Based Data Engineer

5 days ago

Singapore beBeeData Full time $150,000 - $200,000
Job Opportunity

This role involves leading the development and implementation of data engineering solutions to support analytics and AI initiatives. Key responsibilities include designing, developing, and deploying cloud-based products that integrate telco data with other verticals (retail, healthcare).

Key Responsibilities:

  • Developing ETL/ELT workflows (PySpark, Airflow) to ingest, transform, and load data into warehouses (S3, Postgres, Redshift, MongoDB)
  • Automating DataOps/MLOps/LLMOps pipelines with CI/CD (Airflow, GitLab CI/CD, Jenkins), including model training, deployment, and monitoring
  • Designing data models & schemas for large-scale datasets and hybrid cloud architectures
  • Optimizing storage (tables, indexes, partitions, materialized views, columnar encodings) and tuning queries (sort/distribution keys, vacuum)
  • Integrating & enriching telco data with geospatial context, augmenting 5G metrics with legacy 4G, and creating unified time-series datasets
  • Consuming analytics/ML endpoints and real-time streams (Kafka, Kinesis), designing aggregated-data APIs with proper versioning (Swagger/OpenAPI)
  • Managing cloud infrastructure, provisioning and configuring resources (AWS S3, EMR, Redshift, RDS) using IaC (Terraform, CloudFormation), ensuring security (IAM, VPC, encryption)
  • Monitoring performance (CloudWatch, Prometheus, Grafana), defining SLAs for data freshness and system uptime, and automating backups/DR processes
  • Collaborating cross-functionally & documenting data schema, ETL procedures, and runbooks

Requirements:

  • Bachelor's or Master's in Computer Science, Software Engineering, Data Science, or equivalent experience
  • 2+ 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 Big Data frameworks and SQL engines (Spark SQL, Redshift, PostgreSQL) for data marts and analytics
  • Hands-on with Airflow (or equivalent) to orchestrate ETL workflows and GitLab CI/CD or Jenkins for pipeline automation
  • Familiarity with relational (PostgreSQL, Redshift) and NoSQL (MongoDB) 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 (cluster resizing, Spectrum)
  • 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

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