Data Engineer

• Design, build, and deploy large-scale distributed systems and high-throughput data pipelines using Go and cloud-native technologies.
• Lead system-wide architectural decisions, focusing on data flow, performance, and resilience. Actively contribute to the codebase with high-quality code.
• Lead major technical initiatives, reduce technical debt and ensure the platform meets reliability and scalability SLAs. Champion best engineering practices, code quality, testing, and maintainability.
• Collaborate with product and engineering teams and R&D management to define the technical roadmap, review architecture, and mentor junior engineers

Principal Data Engineer

• Design, build, and deploy scalable data pipelines from product and system sources in production, using Python and orchestrators like Airflow.
• Work with distributed query engines such as BigQuery or Athena, with strong SQL throughout.
• Build and maintain semantic data models for large-scale operational systems and data lakes.
• Improve the end-to-end analytics stack, from ingestion to visualization, and collaborate with engineering on event tracking and instrumentation.
• Ensure data quality, consistency, and reliability across the stack.
• Build and maintain dashboards and reporting layers in tools like Looker or Metabase, optimized for performance, usability, and clarity.
• Create self-serve analytics so product and business stakeholders can answer their own questions.
• Support product experimentation: A/B testing, funnel analysis, feature adoption.
• Translate ambiguous questions from product leads, the CEO, the CFO, and others into clear metrics, KPIs, and analytical models.
• Surface trends in usage and user behavior that influence the product roadmap and feature prioritization.
• Provide ad-hoc analysis and strategic reporting for leadership.

Full-Cycle Data Engineer

• Develop, deploy, and maintain scalable, highly reliable, and idempotent ELT data pipelines using Python and orchestration tools like Airflow.
• Lead data transformation and modeling efforts within our cloud data warehouse (e.g., Snowflake, AWS) using dbt, ensuring adherence to modern analytics engineering best practices.
• Architect and grow our centralized semantic layer to establish a "single source of truth" for business metrics.
• Implement rigorous data validation, testing, and monitoring to ensure data integrity.
• Design intuitive, long-term data infrastructure solutions that empower business stakeholders, analysts, and developers to easily and independently query organizational data.
• Partner closely with Backend developers, BI analysts, architects, and business decision-makers to translate complex business requirements into efficient technical architectures.

• Design, implement, and maintain robust, scalable data pipelines for batch and real-time processing using Spark, and other modern tools.
• Own the backend data infrastructure, including ingestion, transformation, validation, and orchestration of large-scale datasets.
• Leverage Google Cloud Platform (GCP) services to architect and operate scalable, secure, and cost-effective data solutions across the pipeline lifecycle.
• Develop and optimize ETL/ELT workflows across multiple environments to support internal applications, analytics, and machine learning workflows.
• Build and maintain data marts and data models with a focus on performance, data quality, and long-term maintainability.
• Collaborate with cross-functional teams including development teams, product managers, and external stakeholders to understand and translate data requirements into scalable solutions.
• Help drive architectural decisions around distributed data processing, pipeline reliability, and scalability.

Senior Data Engineer

• Define and execute the group's data technical roadmap, aligning with Infra, DevOps, and Performance teams
• Design and maintain flexible ETL/ELT frameworks for ingesting, transforming, and classifying cluster verification and telemetry data
• Build and optimize streaming pipelines using Apache Spark, Kafka, and Databricks, ensuring high throughput, reliability, and adaptability to evolving data schemas
• Ensure data quality and pipeline health through observability standards, schema validation, lineage tracking, monitoring, and alerting
• Deliver reliable insights for cluster performance analysis, telemetry visibility, and end-to-end test coverage
• Support self-service analytics for engineers and researchers via Databricks notebooks, APIs, and datasets
• Drive best practices in data modeling, code quality, and operational excellence; collaborate with cross-functional teams to support data-driven decision-making
• Contribute to the development of AI Agents that enhance the visibility and accessibility of insights and data for our users

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Principal Data Engineer

Full-Cycle Data Engineer

Data Engineer

Senior Data Engineer

Senior Data Engineer