Data Engineer

Position Overview

We are seeking a highly capable and commercially minded Data Engineer to join our Singapore team. This role will own the design, development, and reliability of the data models, pipelines, dashboards, and APIs that power our industry research, consulting work, and client-facing analytics products.

You will work closely with lead analysts, researchers, engineering stakeholders, and commercial teams to transform complex, fragmented data sources into accurate, scalable, and decision-useful data products. The ideal candidate is not just a pipeline builder, but someone who can architect robust systems, understand business context, challenge data assumptions, and independently drive solutions from problem definition through production deployment.

This is a high-autonomy role suited for someone who can operate with minimal oversight, make sound technical decisions, and build infrastructure that supports both internal research velocity and external client delivery.

If you have a favorite SCD type — ours is Type 2 — we should probably talk.

Responsibilities

• Own the architecture, development, and maintenance of core data models that support SemiAnalysis’s research, consulting, and client-facing analytics products.
• Design, build, and optimize scalable ETL/ELT pipelines across multiple structured and unstructured data sources.
• Partner with lead analysts to ensure data accuracy, completeness, consistency, and commercial utility across research workflows.
• Translate ambiguous business and research requirements into reliable data models, dashboards, APIs, and analytical tools.
• Maintain and extend internal and external-facing dashboards, APIs, and data delivery systems.
• Establish strong data quality, validation, lineage, observability, and monitoring practices across key datasets and pipelines.
• Improve the performance, reliability, and modularity of existing data infrastructure.
• Support the integration of new datasets, tools, vendors, and infrastructure components to expand the company’s analytics capabilities.
• Build reusable data assets and frameworks that improve analyst productivity and reduce manual workflows.
• Work independently across global time zones while maintaining strong communication with analysts, engineers, and stakeholders.
• Evaluate technical trade-offs pragmatically, balancing speed, accuracy, maintainability, and business impact.
• Act as a senior technical partner to research and commercial stakeholders, ensuring solutions are fit for purpose and scalable.

Requirements

• At least 5–8 years of experience in Data Engineering, Analytics Engineering, Data Science, or a closely related technical role.
• Strong hands-on capability in Python, SQL, and Excel.
• Deep experience designing and maintaining production-grade ETL/ELT pipelines.
• Strong understanding of data modeling concepts, including dimensional modeling, slowly changing dimensions, fact/dimension tables, and data warehouse design.
• Hands-on experience with at least one major cloud platform such as GCP, AWS, or Azure.
• Experience building and maintaining data pipelines, APIs, dashboards, and analytical data products used by business or client-facing teams.
• Strong understanding of data quality, testing, validation, lineage, observability, and pipeline monitoring.
• Ability to work with messy, incomplete, or inconsistent datasets and turn them into clean, trusted, decision-ready data products.
• Highly autonomous and capable of taking a problem from vague requirement to production-ready solution with minimal oversight.
• Strong stakeholder management skills, with the ability to work directly with analysts and business users to clarify requirements and deliver practical solutions.
• Excellent problem-solving ability, technical judgment, and ownership mindset.

Preferred Qualifications

• Experience working in research, consulting, financial services, market intelligence, semiconductors, AI infrastructure, or other data-intensive industries.
• Experience with modern data stack tools such as dbt, Airflow, Dagster, Prefect, BigQuery, Snowflake, Redshift, Databricks, or similar platforms.
• Experience building client-facing data products, APIs, or dashboards.
• Familiarity with software engineering best practices, including Git, CI/CD, testing, documentation, and code review.
• Experience with data governance, access controls, security, and versioning.
• Ability to work across both technical and non-technical teams in a fast-moving, high-trust environment.