Analytics Engineer- Data Operations & Governance

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.

We may use artificial intelligence tools to analyze the content of your Resume/CV against the specific requirements for the position. The purpose is to support our recruitment team in reviewing applications more effectively. These tools assist our recruitment team in their evaluation of your application by providing recommendations, but they do not replace human judgment. Final hiring decisions are ultimately made by humans who consider the insights generated by the tools along with other relevant information. If you would like more details about how your personal information is processed, please contact us.

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