jobs in Optimum Solutions Pte Ltd

Optimum Solutions Pte Ltd Hiring! Full Time Data Engineer (Mid-Level-Senior) in - Ricebowl

Data Engineer (Mid-Level-Senior)

Optimum Solutions Pte Ltd

Undisclosed

Singapore

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Working Location

  • Singapore

Job Description

Responsibilities

Responsibilities


Craft and Execution

  • Design architectures for moderately complex data systems and platforms, with careful consideration for scalability, reliability, and long-term maintainability.
  • Proactively address technical debt through refactoring to maintain system stability over the long term.
  • Implement advanced DataOps practices, including automated pipeline deployment, workflow monitoring, and data observability.
  • Apply advanced ETL/ELT methods to prepare and transform data for complex use cases.
  • Use advanced data modelling approaches to accurately represent complex business processes.
  • Apply advanced data integration methods, including data streaming, CDC, and message queues, when developing data pipelines and components.
  • Effectively manage data privacy, governance, and regulatory requirements, ensuring system designs comply with relevant policies and standards.
  • Develop comprehensive plans to achieve key milestones by clearly defining, breaking down, and prioritising tasks.
  • Communicate and collaborate effectively with team members and stakeholders to ensure coordinated and aligned delivery.


Ownership

  • Take end-to-end ownership of broad projects with ambiguous scope and drive them towards successful outcomes.
  • Take considered risks and use both successes and failures as opportunities to learn and grow.
  • Mentor and support junior team members through knowledge sharing, problem decomposition, and constructive feedback.
  • Contribute actively to improving team productivity and capability through hands-on guidance and support.


Strategic Alignment

  • Translate team objectives into actionable plans by breaking down work and prioritising tasks effectively.
  • Proactively identify opportunities, lead workstreams, and synthesise data into clear recommendations linked to organisational impact.
  • Identify and mitigate project-level risks by anticipating potential challenges before they arise.


Culture and Organisational Influence

  • Coordinate cross-functional collaboration and provide guidance to team members to support effective project delivery.
  • Navigate and help resolve disagreements constructively, facilitating alignment across team members and stakeholders.
  • Constructively challenge existing processes to support continuous improvement and drive change initiatives at both team and division levels.
  • Actively share learnings from both successes and failures, providing recommendations that improve team performance and strengthen a culture of continuous learning.


What we are looking for


Experience and Education

  • Bachelor's degree or higher in Data Science, Computer Science, Statistics, Applied Mathematics, or another related quantitative field.
  • A minimum of 5 years of data engineering experience.
  • Demonstrated experience delivering data platforms or products within large-scale enterprise environments.


Required Skills

  • Strong expertise in data modelling, including OLTP, OLAP, and dimensional modelling approaches.
  • Experience working with cloud data platforms and technologies such as AWS, Azure Synapse/Microsoft Fabric, Snowflake, Databricks, Redshift, and Data Lakes.
  • Proficiency in big data technologies, including Hadoop, Spark, Kafka, and Flink.
  • Strong programming capabilities in Python, Scala, or Java.
  • Experience with CI/CD and DataOps practices, including SHIP-HATS or equivalent frameworks.
  • Strong expertise in data integration approaches, including ETL/ELT, streaming, and APIs.


Preferred Skills

  • Understanding of machine learning and LLM concepts, including prompt engineering and AI application development.
  • Familiarity with MLOps practices and the deployment of machine learning solutions in cloud environments.
  • Awareness of responsible AI principles, including fairness, robustness, and safety.

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