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Singapore
Onsite
Summary
Our talented Data & AI Practice is made up of globally recognized experts - and there’s room for more analytical and ambitious data professionals. If you’re passionate about helping clients make better data-driven decisions to tackle their most complex business issues, let’s talk. Take your skills to a new level and launch a career where you can truly do what matters.
As a Data Engineer within our Data & AI Practice, you’ll leverage a client-centric approach to translate our client’s business strategy into solutions and services that lead to successful, impactful outcomes. You’ll engage in solutioning and support on the most strategic opportunities, intervene in high-risk complex deliveries, and contribute to shape the strategic direction around data architecture. Together we do what matters.
Key Responsibilities
Design, build, and maintain scalable data pipelines for efficient data collection, transformation, and storage.
Develop and optimize ETL/ELT workflows to integrate data from multiple sources into data warehouses or data lakes.
Ensure data quality and integrity through robust validation, cleaning, and monitoring processes.
Collaborate with data scientists, analysts, and business stakeholders to understand data requirements and deliver reliable solutions.
Implement and manage data storage solutions, optimizing for performance, scalability, and cost efficiency.
Monitor and troubleshoot data workflows, resolving issues to maintain high system reliability.
Support data governance, security, and compliance initiatives across the data lifecycle.
Document data processes, architectures, and best practices for ongoing knowledge sharing and team development.
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Skills and experiences
4–6 years of hands-on experience in data engineering, data warehousing, or related fields.
Proficiency in SQL and at least one programming language (Python, Java, or Scala) for data processing and automation.
Experience with ETL/ELT pipeline development and orchestration tools (such as Airflow, Azure Data Factory, Databricks, or similar).
Strong understanding of data modeling, schema design, and database management (relational and NoSQL).
Familiarity with cloud platforms (Azure, AWS, GCP) and big data frameworks (Spark, Hadoop, Kafka).
Experience with data warehousing concepts, building and maintaining data warehouses or lakes.
Knowledge of data quality, validation, and monitoring best practices.
Exposure to CI/CD, version control (Git), and workflow automation in a data engineering context.
Strong analytical, problem-solving, and communication skills; ability to work collaboratively in cross-functional teams
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