Responsibilities
- Serving as the primary technical DRI, defining and executing the long-term technical roadmap to build and sustain a data-driven culture at Plaid.
- Working closely with senior leadership and executives to shape Plaid’s data engineering strategy and roadmap, ensuring alignment with the company’s data-focused product goals and overall vision.
- Deep understanding of Plaid's products and strategy in order to inform the design of Golden Datasets, set data usage principles, and ensure data is structured for maximum impact and usability.
- Focus on delivering well-documented datasets with clearly defined quality metrics, uptime guarantees, and demonstrable usefulness.
- Leading critical data engineering projects that foster collaboration across teams, driving innovation and efficiency throughout the company.
- Mentoring engineers, operations, and data practitioners on best practices for data organization.
- Advocating for the adoption of emerging industry tools and practices, evaluating their fit and implementing them at the right time to keep Plaid at the forefront of data engineering.
- Owning core SQL and Python data pipelines that power our data lake and data warehouse, ensuring their reliability, scalability, and efficiency.
- Upholding Plaid’s commitment to data quality and privacy, embedding these principles into every aspect of data work.
Qualifications
- 10+ years of experience in data engineering, with a proven track record of building scalable data systems and pipelines.
- Experience working with massive datasets (500TB to petabytes) and developing robust data models and pipelines on top of them
- Lead major data modeling, schema design, and data architecture efforts in past roles
- Deep appreciation for schema design and the ability to evolve analytics schemas on top of unstructured data.
- Advanced knowledge of SQL as a flexible, extensible tool and experience with modern orchestration tools like DBT, Mode, and Airflow.
- Hands-on experience with performant data warehouses and lakes such as Redshift, Snowflake, and Databricks.
- Building and maintaining both batch and real-time pipelines using technologies like Spark and Kafka.
- Excited about exploring new technologies and building proof-of-concepts that balance technical innovation with user experience and adoption.
- Enjoy getting into the technical details to manage, deploy, and optimize low-level data infrastructure.
- Champion for data privacy and integrity, and always act in the best interest of consumers.
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