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Full Life Cycle Data Engineering With A High Level of Autonomy

The Best of Both?

How often do you get the best of both worlds? In anything?

It’s almost always a dichotomy. Fast or strong. Good or cheap. Soup or salad.

You sacrifice one thing to get the other.

In data engineering, it’s the place you work.

Either you’re in a swanky Shoreditch startup, a big fish in a small pond, but you’re struggling to stay afloat. All the enthusiasm, none of the capital.

Or

You’re a small cog in a big company machine, with security, but also all the red tape and sluggish processes that mean no one listens to your ideas, even if they are good.

Very rarely do you find the best of both worlds.

The safety of a big player, with the drive, recognition and atmosphere of a more intimate setup.

This wasn’t a tease because there is somewhere exactly like that.

This company provides SaaS that relies heavily on the latest data engineering tools and methods. It’s used by businesses in tonnes of industries, and it’s already regarded as the authority in the field by some of the world’s largest organisations.

They’ve done the whole survival dance, and come out the other side. They’ve got the financial clout.

But it’s still not a huge operation, yet. It still has a start-up feel. Your opinions are listened to, and acted upon, without waiting months like you’d have to in a multinational.

Now, they want make a fundamental change to the world economy by taking their product to the next level. Their mission is to format, connect and contextualise the world of private company information so they can build insights on top.

They can do it, too. But they need you.

Variety, Depth & Breadth Of Work

Your role will be varied.

There are 3 core products, that span marketing, compliance and credit risk. They each have different use cases, so you won’t be working on the same thing every day.  The types of challenges you’ll get to tackle include:

  • Expansion of core companies datasets to build an offering using variety of tools like Spark, Airflow and BigQuery running in Google Cloud Platform.
  • Large scale web crawling and NLP analysis for industry tagging, including employing different PoS tagging models, topic modelling and vector embedding techniques such as word2vec.
  • Data matching and augmentation as a service, running in async architecture and backed by ML models.
  • Building new state of the art methods in network embeddings (network to vector space projections).

You’ll work with the latest tech stack, so you’ll pick up new skills and hone the ones you already have.

Unusually High Levels of Autonomy

Because this is a smaller environment, there’s no micromanagement.  You’ll be solving data problems in your own way.

You’ll have a high degree of autonomy to decide on the architecture and tools to use when building solutions.

Your role can encompass the whole data lifecycle, if you want it to. That’s from data cleansing and processing, to building machine learning algorithms, through to delivering insights to the end-user.

You’ll also have complete ownership of deployment aspects of your solutions, functional as well as performance testing. This company encourages collective ownership of Infrastructure across engineering.

In short – No-one else gets to fiddle with your stuff.

What You need to have:

Look, it’s a job. Like anything, it’s not going to be perfect for everyone.

But if you’ve ever felt like you’ve had to settle in your career, maybe this is worth finding out about.

You can – at least – find out more, if you’ve got strong experience in:

  • Data engineering – designing system architecture and software
  • Python, Scala or Java
  • Spark or MapReduce
  • Distributed Messaging & Version Control
  • Expert SQL skills

Hit the Easy Apply button below.

Who knows, maybe the best of both worlds really does exist.

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