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Senior Data Engineer, Machine Learning Platform

Attentive® is the AI-powered mobile marketing platform transforming the way brands personalize consumer engagement. Attentive enables marketers to craft tailored journeys for every subscriber, driving higher recurring revenue and maximizing campaign performance. Activating real-time data from multiple channels and advanced AI, the platform personalizes content, tone, and timing to deliver 1:1 messages that truly resonate.


With a top-rated customer success team recognized on G2, Attentive partners with marketers to provide strategic guidance and optimize SMS and email campaigns. Trusted by leading global brands like Neiman Marcus, Samsung, Wayfair, and Dyson, Attentive ensures enterprise-grade compliance and deliverability, supporting trillions of interactions across more than 70 industries. To learn more or request a demo, visit www.attentive.com or follow us on LinkedIn, X (formerly Twitter), or Instagram.


Attentive’s growth has been recognized by Deloitte’s Fast 500, Linkedin’s Top Startups and Forbes Cloud 100 all thanks to the hard work from our global employees!


About the Role

We’re looking for a self-motivated, highly driven Senior Software Engineer to join our Machine Learning Operations (MLOps) team. As a team, we enable Attentive’s Machine Learning (ML) practice to directly impact Attentive’s AI product suite through the tools to train, inference, and deploy ML models with higher velocity and performance, while maintaining reliability. We build and maintain a foundational ML platform spanning the full ML lifecycle for consumption by ML engineers and data scientists. This is an exciting opportunity to join a rapidly growing MLOps team at the ground floor with the ability to drive and influence the architectural roadmap enabling the entire ML organization at Attentive.


This team and role is responsible for building and operating the ML data, tooling, serving, and inference layers of the ML platform. We are excited to bring on more engineers to continue expanding this stack. 


What You'll Accomplish
  • Unlock offline & real-time access to trillions of data points for our ML and Data Science teams
  • Manage, expand, and optimize our feature store that enables feature engineering, multi-TB scale training jobs, and offline / real-time inferencing
  • Support PB scale data operations on the feature store using Apache Spark, Spark Structured Streaming, Kafka, and Ray
  • Partner with other teams and business stakeholders to deliver ML and AI initiatives


Your Expertise
  • You have been working in the areas of Data Engineering / MLOps for 5+ years, and have built and matured the pipelines of a PB-scale feature store
  • You have deep Apache Spark, Spark Streaming, and Ray Data experience and built data pipelines for ML use cases using these tools
  • You understand the correlation between data cardinality, query plans, configuration settings, and hardware and the impact of each on data pipeline performance 
  • You have led the rollout and operationalization of feature stores such as Tecton or Feast
  • You understand the key differences between online and offline ML inferences and can voice the critical elements to be successful with each to meet business needs


What We Use
  • Our infrastructure runs primarily in Kubernetes hosted in AWS’s EKS
  • Infrastructure tooling includes Istio, Datadog, Terraform, CloudFlare, and Helm
  • Our backend is Java / Spring Boot microservices, built with Gradle, coupled with things like DynamoDB, Kinesis, AirFlow, Postgres, Planetscale, and Redis, hosted via AWS
  • Our frontend is built with React and TypeScript, and uses best practices like GraphQL, Storybook, Radix UI, Vite, esbuild, and Playwright
  • Our automation is driven by custom and open source machine learning models, lots of data and built with Python, Metaflow, HuggingFace 🤗, PyTorch, TensorFlow, and Pandas


You'll get competitive perks and benefits, from health & wellness to equity, to help you bring your best self to work.


For US based applicants:

- The US base salary range for this full-time position is $144,840 - $210,000 annually + equity + benefits

- Equity is a substantial part of the total compensation package

- Our salary ranges are determined by role, level, and location


#LI-EZ1


Attentive Company Values

Default to Action - Move swiftly and with purpose

Be One Unstoppable Team - Rally as each other’s champions

Champion the Customer - Our success is defined by our customers' success

Act Like an Owner - Take responsibility for Attentive’s success


Learn more about AWAKE, Attentive’s collective of employee resource groups.


If you do not meet all the requirements listed here, we still encourage you to apply! No job description is perfect, and we may also have another opportunity that closely matches your skills and experience.


At Attentive, we know that our Company's strength lies in the diversity of our employees. Attentive is an Equal Opportunity Employer and we welcome applicants from all backgrounds. Our policy is to provide equal employment opportunities for all employees, applicants and covered individuals regardless of protected characteristics. We prioritize and maintain a fair, inclusive and equitable workplace free from discrimination, harassment, and retaliation. Attentive is also committed to providing reasonable accommodations for candidates with disabilities. If you need any assistance or reasonable accommodations, please let your recruiter know. 

Average salary estimate

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$144840K
$210000K

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What You Should Know About Senior Data Engineer, Machine Learning Platform, Attentive

As a Senior Data Engineer at Attentive®, you're stepping into an innovative role that influences the very fabric of our machine learning platform. In this position, you will work closely with the Machine Learning Operations (MLOps) team, playing a crucial role in creating and optimizing a full lifecycle for machine learning, from training to deployment. Your mission? To enable seamless access to trillions of data points that will supercharge our ML and Data Science teams. You’ll have the opportunity to manage and enhance our feature store, ensuring that it operates efficiently, whether that's handling massive offline training jobs or providing real-time inference capabilities. We believe in a collaborative spirit, so you’ll need to partner with various teams and stakeholders to bring our AI initiatives to life. With over five years of experience in Data Engineering or MLOps under your belt, you’ll utilize tools like Apache Spark and Kafka to build efficient pipelines. Your knowledge will influence architectural decisions and drive our operations forward, ensuring that our machine learning products deliver immense value to our extensive list of clients. Join us, and let’s continue to reshape the way brands engage with their customers through data-driven insights and personalized experiences while working in a supportive environment that values your expertise and fosters professional growth.

Frequently Asked Questions (FAQs) for Senior Data Engineer, Machine Learning Platform Role at Attentive
What are the key responsibilities of a Senior Data Engineer at Attentive?

As a Senior Data Engineer at Attentive, your main responsibilities include managing and optimizing our feature store, unlocking access to massive datasets for our Machine Learning teams, and ensuring the reliability of ML data processes. You will also collaborate with various teams to support the operationalization of AI initiatives, driving architectural advancements that enhance our machine learning capabilities.

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What qualifications are required for the Senior Data Engineer position at Attentive?

To be considered for the Senior Data Engineer role at Attentive, you should have over five years of experience in Data Engineering or MLOps. Candidates need a robust understanding of tools like Apache Spark, Kafka, and Ray, alongside experience building and maintaining PB-scale feature stores. Familiarity with both online and offline machine learning inferences is crucial.

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What technologies will a Senior Data Engineer at Attentive work with?

In the Senior Data Engineer role at Attentive, you'll work with a range of technologies, including Apache Spark, Spark Streaming, and streaming tools like Kafka, as well as databases such as PostgreSQL and DynamoDB. The infrastructure mainly runs on Kubernetes hosted in AWS, using various DevOps tools like Terraform and Datadog to create a robust operational environment.

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What is the company culture like for Senior Data Engineers at Attentive?

The company culture at Attentive places a strong emphasis on collaboration, innovation, and accountability. As a Senior Data Engineer, you will be part of a supportive team that encourages continuous learning and values each member's contributions towards achieving collective success. Our core values include moving swiftly, championing each other, and focusing on our customers' needs.

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How does the Senior Data Engineer role contribute to Attentive's overall mission?

The Senior Data Engineer at Attentive plays a vital role in enhancing our machine learning capabilities that drive personalized customer engagements. By optimizing data processes and building foundational ML infrastructure, you'll help ensure that our AI-powered platform can effectively respond to user needs, thereby fulfilling Attentive's mission of transforming consumer engagement through intelligent marketing solutions.

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Common Interview Questions for Senior Data Engineer, Machine Learning Platform
Can you describe your experience with Apache Spark as a Senior Data Engineer?

Highlight specific projects where you utilized Apache Spark to process large datasets. Discuss how you managed data pipelines and dealt with performance optimization, demonstrating your hands-on experience and understanding of Spark's capabilities.

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What strategies do you use to optimize data pipelines?

Explain your approach to data pipeline optimization, including monitoring performance metrics, adjusting configurations, and employing tools like Spark or Kafka to increase throughput. Providing examples from past work experiences can illustrate your methods effectively.

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How do you handle real-time data processing challenges?

Discuss your techniques for managing real-time data streams, perhaps with examples of streaming technologies you've worked with, such as Spark Streaming or Kafka. Elaborate on the challenges you faced and how you overcame them in previous roles.

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Explain the importance of feature stores in machine learning.

Discuss how feature stores serve as centralized repositories for ML features, enabling consistent access for training and inference. Elaborate on your experience creating or maintaining feature stores and how they contribute to the overall efficiency of ML workflows.

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What are some challenges you’ve faced when scaling data operations?

Be sure to outline specific examples where you encountered scaling challenges. Discuss what solutions you implemented and how they positively impacted data processing capabilities, demonstrating your problem-solving skills.

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How do you ensure data quality in your projects?

Describe your data validation processes and methodologies to maintain data integrity, emphasizing any frameworks or practices you implement to ensure your datasets are clean, accurate, and reliable.

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What role do you think MLOps plays in successful machine learning deployments?

Explain your view of MLOps as a critical bridge between data engineering and ML model deployment, emphasizing how effective MLOps practices lead to smoother collaboration and improved model performance in production environments.

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Can you share your experience with cloud-based infrastructures?

Highlight your experience working with cloud platforms, particularly AWS, and how you’ve utilized services such as EKS for Kubernetes or database management solutions in your past projects. Mention how cloud infrastructures can enhance the performance and scalability of ML systems.

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How do you approach troubleshooting in complex data pipelines?

Outline your systematic approach to troubleshooting, starting from log analysis to pinpoint underlying issues. Provide an example of a complex problem you resolved in a previous role, showcasing your analytical thinking skills.

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What innovations would you bring as a Senior Data Engineer at Attentive?

Discuss any latest trends or tools in data engineering or MLOps you believe could benefit Attentive. Propose innovative ideas tailored to the company’s needs and explain how implementing these could enhance efficiency and performance.

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Create magical conversations. DEFAULT TO ACTION - Speed is our best offense and defense -We don't wait for opportunities to come to us, we create them through our proactive mindset. -We believe in making quick decisions, executing strategies, and...

285 jobs
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VIEW MATCH
CULTURE VALUES
Passion for Exploration
Dare to be Different
Customer-Centric
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Inclusive & Diverse
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Full-time, on-site
DATE POSTED
March 28, 2025

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