Analytics Engineer- Data Operations & Governance

P2P; Singapore; On-site; SGD 70,000 - 90,000; Full time

Job summary

A prominent data solutions company in Singapore is seeking a Data Engineer to manage data operations and governance. Responsibilities include building data pipelines, maintaining data accuracy, and developing internal tools using SQL and Python. The ideal candidate has expertise in data governance and strong collaboration skills. Competitive salary and opportunities for professional growth offered.

Qualifications

• Strong SQL and proficiency in Python or JavaScript.
• Experience with data engineering tools like dbt and Airflow.
• Designing data governance practices in analytics environments.

Responsibilities

• Own the accuracy and governance of product and user-event data.
• Build and maintain scalable data pipelines.
• Develop internal data tools and automate workflows.

Skills

SQL

Python

Data Governance

Collaboration

Tools

Airflow

dbt

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.