Analytics Engineer- Data Operations & Governance

Medium; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

A prominent tech company in Singapore is looking for a Data Engineer. This role requires strong SQL skills, proficiency in Python or JavaScript, and experience with data governance and modern data engineering tools. You will own product data accuracy, build and maintain data pipelines, and develop internal data tools for operational efficiency. The ideal candidate will have experience in collaboration and communication with cross-functional teams.

Qualifications

• Strong SQL and working proficiency in Python or JavaScript.
• Experience with modern data engineering tools like dbt and Airflow.
• Track record designing or maintaining data governance practices.
• Strong written and verbal communication skills.
• Familiarity with anomaly detection and data validation.

Responsibilities

• Own the accuracy and reliability of product and user-event data.
• Build and maintain scalable, observable data pipelines.
• Develop and maintain internal data tools and dashboards.
• Operate and enhance data scraping workflows for competitive intelligence.

Skills

Strong SQL

Python proficiency

JavaScript proficiency

Data governance practices

Data pipeline optimization

Version control (Git)

Anomaly detection

Database modeling

Tools

dbt

Airflow

Fivetran

Dagster

Job description

Responsibilities:

• Data Operations & Governance: Own the accuracy, reliability, and structure of product and user-event data through robust governance practices; Define and enforce standards for event tracking, data schemas, and documentation across teams; Conduct regular audits, validation checks, and coordinate instrumentation changes with engineering and product teams.
• Data Pipeline Development & Maintenance: Build and maintain scalable, observable data pipelines using tools like dbt, Airflow, or similar frameworks; Monitor pipeline health, implement alerting systems, and resolve data issues with root cause analysis; Optimize pipeline performance and ensure high availability of core datasets for analytics and reporting.
• Internal Tooling & Automation: Develop and maintain internal data tools, utilities, and dashboards using SQL, Python, and lightweight web technologies; Automate workflows to reduce manual reporting and improve operational efficiency for data stakeholders; Create reusable data models that support fast iteration and confident self-service analysis.
• Competitive Intelligence & Data Collection: Operate and enhance data scraping workflows to collect structured information on competitors, pricing, and market trends; Ensure scraping systems are stable, maintainable, and compliant with data privacy and ethical standards.

Requirements:

• Engineering Foundation: Strong SQL and working proficiency in Python or JavaScript for building and maintaining data infrastructure; Experience with modern data engineering tools (e.g., dbt, Airflow, Fivetran, Dagster); Familiarity with version control (Git), code modularization, and documentation practices.
• Data Quality & Governance Experience: Track record designing or maintaining data governance practices in product analytics environments (e.g., Segment, GA4, Mixpanel); Comfortable building QA checks, anomaly detection, and data validation processes; Familiarity with data governance education and data governance related stakeholder management.
• Operational Mindset: Comfortable being on point for data issues, debugging pipeline failures, and ensuring continuity in reporting and dashboards; Ability to set up alerting/logging mechanisms to proactively detect and respond to data problems.
• Collaboration & Communication: Strong written and verbal communication skills to align with product, engineering, and business teams; Able to translate business questions into engineering requirements and technical work into stakeholder-friendly language.
• Preferred Qualifications: Prior experience / knowledge on data science / machine learning; Prior experience on hands-on data engineering; Understanding of data operation & governance in analytics workflows; Experience supporting data for experimentation or A/B testing pipelines.