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Analytics Engineer- Data Operations
2 weeks ago
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. #J-18808-Ljbffr
