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Data Engineer Job Opening In Singapore, Singapore – Now Hiring StarHub


Job description

Job Description

As a Data Engineer, you’ll work with large-scale, heterogeneous datasets and hybrid cloud architectures to support analytics and AI solutions.

Collaborate with data scientists, infra engineers, sales specialists, and stakeholders to ensure data quality, build scalable pipelines, and optimize performance.

Your work will integrate telco data with other verticals (retail, healthcare), automate DataOps/MLOps/LLMOps workflows, and deliver production-grade systems.

As a Data Engineer, you will:

  • Ensure Data Quality & Consistency

  • Validate, clean, and standardize data (e.g., geolocation attributes) to maintain integrity.

  • Define and implement data quality metrics (completeness, uniqueness, accuracy) with automated checks and reporting.
  • Build & Maintain Data Pipelines

  • Develop ETL/ELT workflows (PySpark, Airflow) to ingest, transform, and load data into warehouses (S3, Postgres, Redshift, MongoDB).

  • Automate DataOps/MLOps/LLMOps pipelines with CI/CD (Airflow, GitLab CI/CD, Jenkins), including model training, deployment, and monitoring.
  • Design Data Models & Schemas 

  • Translate requirements into normalized/denormalized structures, star/snowflake schemas, or data vaults.

  • Optimize storage (tables, indexes, partitions, materialized views, columnar encodings) and tune queries (sort/distribution keys, vacuum).
  • Integrate & Enrich Telco Data

  • Map 4G/5G infrastructure metadata to geospatial context, augment 5G metrics with legacy 4G, and create unified time-series datasets.

  • Consume analytics/ML endpoints and real-time streams (Kafka, Kinesis), designing aggregated-data APIs with proper versioning (Swagger/OpenAPI).
  • Manage Cloud Infrastructure

  • Provision and configure resources (AWS S3, EMR, Redshift, RDS) using IaC (Terraform, CloudFormation), ensuring security (IAM, VPC, encryption).

  • Monitor performance (CloudWatch, Prometheus, Grafana), define SLAs for data freshness and system uptime, and automate backups/DR processes.
  • Collaborate Cross-Functionally & Document

  • Clarify objectives with data owners, data scientists, and stakeholders; partner with infra and security teams to maintain compliance (PDPA, GDPR).

  • Document schemas, ETL procedures, and runbooks; enforce version control and mentor junior engineers on best practices.

  • Requirements


    · Degree 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


    · Familiar 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

    Nice to Have


    · 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 LLM-based data applications (NL-->SQL generation)


    · Participation in GenAI POCs (RAG pipelines, Agentic AI demos, geomobility analytics)


    · Client-facing or stakeholder-management experience in data-driven/AI projects

    Required Skill Profession

    Computer Occupations


    • Job Details

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    Unlock Your Data Engineer Potential: Insight & Career Growth Guide


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    Explore profound insights with Expertini's real-time, in-depth analysis, showcased through the graph here. Uncover the dynamic job market trends for Data Engineer in Singapore, Singapore, highlighting market share and opportunities for professionals in Data Engineer roles.

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    Are You Looking for Data Engineer Job?

    Great news! is currently hiring and seeking a Data Engineer to join their team. Feel free to download the job details.

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    The Work Culture

    An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at StarHub adheres to the cultural norms as outlined by Expertini.

    The fundamental ethical values are:

    1. Independence

    2. Loyalty

    3. Impartiapty

    4. Integrity

    5. Accountabipty

    6. Respect for human rights

    7. Obeying Singapore laws and regulations

    What Is the Average Salary Range for Data Engineer Positions?

    The average salary range for a varies, but the pay scale is rated "Standard" in Singapore. Salary levels may vary depending on your industry, experience, and skills. It's essential to research and negotiate effectively. We advise reading the full job specification before proceeding with the application to understand the salary package.

    What Are the Key Qualifications for Data Engineer?

    Key qualifications for Data Engineer typically include Computer Occupations and a list of qualifications and expertise as mentioned in the job specification. The generic skills are mostly outlined by the . Be sure to check the specific job listing for detailed requirements and qualifications.

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    Interview Tips for Data Engineer Job Success

    StarHub interview tips for Data Engineer

    Here are some tips to help you prepare for and ace your Data Engineer job interview:

    Before the Interview:

    Research: Learn about the StarHub's mission, values, products, and the specific job requirements and get further information about

    Other Openings

    Practice: Prepare answers to common interview questions and rehearse using the STAR method (Situation, Task, Action, Result) to showcase your skills and experiences.

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    During the Interview:

    Be Punctual: Arrive on time to demonstrate professionalism and respect.

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    Turn Off Phone: Avoid distractions during the interview.

    Final Thought:

    To prepare for your Data Engineer interview at StarHub, research the company, understand the job requirements, and practice common interview questions.

    Highlight your leadership skills, achievements, and strategic thinking abilities. Be prepared to discuss your experience with HR, including your approach to meeting targets as a team player. Additionally, review the StarHub's products or services and be prepared to discuss how you can contribute to their success.

    By following these tips, you can increase your chances of making a positive impression and landing the job!

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