Solution Architect

How we see this role

Scribble Data provides a foundational building block, our feature store, called Enrich, on top of which customers build their ML models (i.e. they use feature matrices from Enrich to train their ML models) and whose datasets aid their deep analytics. Our customers trust us with their data and the outputs, and expect cutting edge tools and thought processes from us. Product will be at the center of the relationship.

 

The Solution Architect role  owns this relationship with the customer, and the delivery. Each customer of Scribble Data is hard-won and creates exponential value for us. As a consequence, managing the delivery to these customers, guiding their data science teams on best practices, advising them on their data roadmap, helping them build datasets, all the while communicating with their leadership, is critical to our success.

What You Will Do
  • The Solution Architect role is hands-on. The role begins at the preliminary conversation stage with new customer prospects, helping frame their problems into the scope for a PoC, building and owning this PoC (always developed atop our feature store, Enrich), converting them into long-term customers, and then owning the delivery and relationship end-to-end. You will be supported by Scribble data’s team at every step. 

  • You will be focused on delivery for each customer, with high attention-to-detail. You will be responsible for ensuring trust, robustness, performance, and ease of use of the platform in each customer’s context, and depending on the size of the individual customer engagement, you may lead a team of engineers as well. 

  • You'll need to manage people and vendors. You might even need to travel, look squarely into the eyes of many customers and sell. You'll often receive feedback and new asks from customers and be able to work that into your delivery.

  • You will represent Scribble Data at public forums, create talks for conferences, and help build an engineering brand to attract the best of the talent, while contributing to shaping the public conversation around feature stores and related topics. 

  • You will also track and research emerging trends in the space, as well as moves that our competitors are making. These should help create nice feedback loops for our product team.

Your Mission
  • At pre-sales stages, you will be required to understand the domains of our prospective customers, and their specific context to help the business development team make better pitches.

  • At the sales stage, alongside the Business Development team, you will have to listen and learn about the customer’s journey, pain points and aspirations, and use that to frame and convey where Scribble can add value.

  • You will need to demo the Enrich feature store, and this could include travel.

  • As part of delivery, you will be responsible for development and support of pipelines, dashboards, and other data artifacts atop the Enrich platform, for each individual customer. You may also have to act in an advisory capacity, to build out data roadmaps for individual customers. You will also need to manage this activity as a project.

  • You will have to hire, mentor, and grow a team of world-class ML engineers.

  • Communicate clearly about Enrich’s engineering capabilities and limitations, and collaborate productively with customers, vendors, and business stakeholders.   

Your Strengths
  • Proven record of hands-on data science and/or ML engineering experience.

  • 7+ years of industry experience owning and building data infrastructure including packaging, deploying, and monitoring data systems.

  • Having built and shipped a product at a startup will be a plus.

  • Masters degree or higher in Computer Science.

  • Strong system-level thinking and problem solving skills with the ability to make architecture decisions as well as deep dive on code at the line level.

  • Python implementation experience, with experience in another language like Java.

  • Proficiency in designing, implementing and automating systems for data workflow management.

  • Experience designing robust, scalable data architectures.

  • Proficiency in developing and publishing APIs using web services.

  • Proficiency in using distributed storage (e.g. S3), serverless architecture (e.g. Lambda), streaming data (e.g. Kafka), scalable search (e.g. Elasticsearch).

  • Proficiency with containerization (e.g. Docker/Kubernetes), and other deployment tooling.

  • Proficiency with big data technologies like map-reduce, NoSQL, Spark, HBase, Hive.

  • Experience with data security protocols, data access controls, AWS/GCP/Azure security mechanisms at the data and access levels, database security, scripting attacks, API security.

  • Strong communication and collaboration skills to work with stakeholders from different backgrounds.

You will love this job if
  • You like an adventure, and have a stomach for learning and unlearning everyday. 

  • You dream big and believe that hard problems need streamlined systems and massive amounts of data to tackle effectively.

  • You have the discipline to take audacious goals and break down years-long roadmaps into near term deliverables that provide value to business stakeholders.

  • You embrace uncertainty and can develop processes and systems to handle rapidly evolving  requirements.

  • You enjoy educating your customers and team members about what it means to be data-oriented and cultivating engineering best practices.

  • You are comfortable selling to customers (not a biggie).

  • You have a sense of humour.

  • You don’t mind getting coffee for the rest of the team, and understand that this JD is just directional - things will change, as will expectations at the company. The old start-up stereotype holds true even here -  you’re willing to wear multiple hats, right?

APPLY NOW

Send your cover letter and CV to dominique@scribbledata.io

hello@scribbledata.io

+1-720-445-8387

2074 (#17) 16 D Main,

H.A.L 2nd Stage,

Indiranagar,

Bangalore 560008

2035, Sunset Lake Road, Suite B-2,

Newark, DE - 19702

CIN: U72900KA2016PTC097462

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