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Change the future of video analysis and intelligence with Alison.ai!

Stop the guesswork. End the A/B testing. Produce results-driven creative faster than ever with Alison, the world's best AI-powered video creative analysis platform. From ideation to optimization to production, Alison makes it easier than ever to create assets such as videos, banners, and playable ads with the highest likelihood of success. Our unique SaaS technology allows businesses to identify their top-performing elements in creatives, get full-funnel data, and perform intelligent competitive analysis.

About the Role

We are seeking talented and passionate Data Engineer to join our growing Data team. In this pivotal role, you will be instrumental in designing, building, and optimizing the critical data infrastructure that underpins Alison.ai's innovative creative intelligence platform. You will tackle complex data challenges, ensuring our systems are robust, scalable, and capable of delivering high-quality data to power our advanced AI models, customer-facing analytics, and internal business intelligence. This is an opportunity to make a significant impact on our product, contribute to a data-driven culture, and help solve fascinating problems at the intersection of data, AI, and marketing technology.

Key Responsibilities

  • Architect & Develop Data Pipelines: Design, implement, and maintain sophisticated, end-to-end data pipelines for ingesting, processing, validating, and transforming large-scale, diverse datasets.
  • Manage Data Orchestration: Implement and manage robust workflow orchestration for complex, multi-step data processes, ensuring reliability and visibility.
  • Advanced Data Transformation & Modeling: Develop and optimize complex data transformations using advanced SQL and other data manipulation techniques. Contribute to the design and implementation of effective data models for analytical and operational use.
  • Ensure Data Quality & Platform Reliability: Establish and improve processes for data quality assurance, monitoring, alerting, and performance optimization across the data platform. Proactively identify and resolve data integrity and pipeline issues.
  • Cross-Functional Collaboration: Partner closely with AI engineers, product managers, developers, customer success and other stakeholders to understand data needs, integrate data solutions, and deliver features that provide exceptional value.
  • Drive Data Platform Excellence: Contribute to the evolution of our data architecture, champion best practices in data engineering (e.g., DataOps principles), and evaluate emerging technologies to enhance platform capabilities, stability, and cost-effectiveness.
  • Foster a Culture of Learning & Impact: Actively share knowledge, contribute to team growth, and maintain a strong focus on how data engineering efforts translate into tangible product and business outcomes.

What We’re Looking For

  • 3+ years of experience as a Data Engineer, building and managing complex data pipelines and data-intensive applications.
  • Solid understanding and application of software engineering principles and best practices. Proficiency in a relevant programming language (e.g., Python, Scala, Java) is highly desirable.
  • Deep expertise in writing, optimizing, and troubleshooting complex SQL queries for data transformation, aggregation, and analysis in relational and analytical database environments.
  • Hands-on experience with distributed data processing systems, cloud-based data platforms, data warehousing concepts, and workflow management tools.
  • Strong ability to diagnose complex technical issues, identify root causes, and develop effective, scalable solutions.
  • A genuine enthusiasm for tackling new data challenges, exploring innovative technologies, and continually expanding your skillset.
  • A keen interest in understanding how data powers product features and drives business value, with a focus on delivering results.
  • Excellent ability to communicate technical ideas clearly and work effectively within a multi-disciplinary team environment.

Advantages

  • Familiarity with the marketing/advertising technology domain and associated datasets.
  • Experience with data related to creative assets, particularly video or image analysis.
  • Understanding of MLOps principles or experience supporting machine learning workflows.



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