Who Are You

We are seeking a highly skilled and experienced ML Platform Architect to join our dynamic and growing data platform team. As an ML Platform Architect, you will play a crucial role in strengthening and expanding the core of our data products. We want you to help us scale our business, to data-driven decisions, and to contribute to our overall data strategy. You will work alongside talented data platform engineers to envision how all the data elements from multiple sources should fit together and then execute that plan. The ideal candidate must: 

• Effectively communicate complex data problems by tailoring the message to the audience and presenting it clearly and concisely. 
• Balance multiple perspectives, disagree, and commit when necessary to move key company decisions and critical priorities forward.
• Have a profound comprehension of data quality, governance, and analytics.
• Have the ability to work independently in a dynamic environment and proactively approach problem-solving.
• Be committed to driving positive business outcomes through expert data handling and analysis.
• Be an example for fellow engineers by showcasing customer empathy, creativity, curiosity, and tenacity.
• Have strong analytical and problem-solving skills, with the ability to innovate and adapt to fast-paced environments.
• Design and build clear, understandable, simple, clean, and scalable solutions.

What You’ll Do

• Modernize Signifyd’s Machine Learning (ML) Platform to scale for resiliency, performance, and operational excellence working closely with Engineering and Data Science teams across Signifyd’s R&D group.
• Create and deliver a technology roadmap focused on advancing our data processing capabilities, which will support the evolution of our real-time data processing and analysis capabilities.
• Work alongside ML Engineers, Data Scientists, and other Software Engineers to develop innovative big data processing solutions for scaling our core product for eCommerce fraud prevention.
• Take full ownership of significant portions of our data processing products, including collaborating with stakeholders on machine learning models, designing large-scale data processing solutions, creating additional processing facets and mechanisms, and ensuring the support of low-latency, high-quality, high-scale decisioning for Signifyd’s flagship product.
• Architect, deploy, and optimize Databricks solutions on AWS, developing scalable data processing solutions to streamline data operations and enhance data solution deployments.
• Implement data processing solutions using Spark, Java, Python, Databricks, Tecton, and various AWS services (S3, Redshift, EMR, Athena, Glue).
• Mentor and coach fellow engineers on the team, fostering an environment of growth and continuous improvement.
• Identify and address gaps in team capabilities and processes to enhance team efficiency and success.

What You’ll Need

• Ideally has over 10 years of experience in data engineering, including at least 5 years of experience as a data or machine learning architect or lead. Have successfully navigated the challenges of working with large-scale data processing systems.
• Deep understanding of data processing, comfortable working with multi-terabyte datasets, and skilled in high-scale data ingestion, transformation, and distributed processing, with strong Apache Spark or Databricks experience.
• Experience in building low-latency, high-availability data stores for use in real-time or near-real-time data processing with programming languages such as Python, Scala, Java, or JavaScript/TypeScript, as well as data retrieval using SQL and NoSQL.
• Hands-on expertise in data technologies with proficiency in technologies such as Spark, Airflow, Databricks, AWS services (SQS, Kinesis, etc.), and Kafka. Understand the trade-offs of various architectural approaches and recommend solutions suited to our needs.
• Experience with the latest technologies and trends in Data, ML, and Cloud platforms.
• Demonstrable ability to lead and mentor engineers, fostering their growth and development. 
• You have successfully partnered with Product, Data Engineering, Data Science and Machine Learning teams on strategic data initiatives.
• Commitment to quality, you take pride in delivering work that excels in data accuracy, performance and reliability, setting a high standard for the team and the organization.

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