Analytics Engineer- Data Operations & Governance

Medium; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A leading tech company in Singapore seeks a Data Engineer to manage data operations and ensure the quality of product and user-event data. You will develop robust data pipelines, oversee data governance practices, and collaborate with cross-functional teams. Strong SQL skills and experience with data engineering tools like dbt and Airflow are essential. Join us to optimize data workflows and enhance operational efficiency.

Qualifications

• Strong SQL and proficiency in Python or JavaScript for data infrastructure.
• Track record in designing data governance practices in product analytics.
• Ability to debug pipeline failures and ensure continuity in reporting.

Responsibilities

• Ensure accuracy, reliability, and structure of product and user-event data.
• Build and maintain scalable data pipelines using tools like dbt and Airflow.
• Develop and maintain internal data tools and automate workflows.

Skills

Strong SQL

Python

Data governance practices

Collaboration & Communication

Tools

dbt

Airflow

Git

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.