Senior Software Engineer (Python), Online Fashion Retail

New York
US$120000 - US$170000 per year + Bonus

THE COMPANY

My Client is an online luxury fashion retail platform born out of a deep love of fashion and a profound belief that fashion is an essential expression of individuality and what makes each of us unique. Founded in 2007, their mission is to be the global technology platform for luxury fashion, connecting creators, curators and consumers.

They operate a modular end-to-end technology platform purpose built to connect the luxury fashion ecosystem worldwide. Their vision was to create a single operating system that could address the complex demands of consumers and luxury sellers alike. Their platform is built on an API-enabled proprietary technology stack, which provides the foundation for the three main components: applications, services and data.

THE ROLE

They rely heavily on insights gained from data to drive customer acquisition and retention and on metrics to detect new areas of improvement. As a Senior Software Engineer, you'll contribute to the engineering of the next-generation data science products and interfaces that connect their Machine Learning technology to the vast ecosystem of internal and external applications. This is an exciting time to join the business, with their recent acquisitions and IPO back in 2018, you'll have the chance to be a part of a brand-new function and work exclusively on large scale, high-impact projects.

They're looking for an experienced Senior Engineer, designing and leading the development of large-scale projects.

Frequent technologies used; Python (SQLAlchemy, Django, Flask, scikit-learn, PyTorch, NumPy), Airflow, Celery, Cassandra, MySQL, BigQuery, PostgresSQL, Docker, Elasticsearch, Google Cloud Service (Google Compute Engine) / AWS, PyTest, Spark, Terraform, Ansible

Primary Responsibilities:

  • Collaboratively design, build, and maintain Python micro-services in our cloud data platform.
  • Work with other Software Engineers, Data Scientists, and Data Engineers to deliver efficient data products.
  • Provide creative ideas to improve core data products.
  • Be responsible for the health of production systems.
  • Design and develop scalable and performant services with strong emphasis in algorithmic design and scalability.
  • Have a team-oriented mindset and seek to share knowledge and uplift the rest of the team

YOUR SKILLS AND EXPERIENCE

  • Experienced in designing large scale systems that take advantage of Big Data technologies like Spark, BigQuery, etc.
  • Advanced in Python and experienced in the creation of services using frameworks like Flask/Django.
  • Disciplined in approach to testing (TDD/BDD, code review, documentation).
  • Experienced in distributed systems, service-oriented architectures, design and implementation of high throughput, low latency applications.
  • Experienced in containerization tools like Docker and configuration management tools like Ansible.
  • Experienced building and scaling batch/asynchronous systems. Using tools like Airflow/Celery/RabbitMQ.

HOW TO APPLY

Please register your interest by sending your CV/resume to Peterson Hyppolite via the Apply link on this page. Or if you know someone better suited for this role, please feel free to share this posting.

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55652
New York
US$120000 - US$170000 per year + Bonus
  1. Permanent
  2. Software Engineer

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Visit our Blogs & News portal or check out our recent posts below.

A Data Engineer is a Unique Blend of Data Professional

From startup and small business to large enterprises, each type of business requires a unique blend of Data professional. Though in today’s world, much of the Data being gathered, catalogued, and analyzed happens both in the Cloud and on a hard drive, each type of business has a different need, budget, goals, and objectives. But there is one thing each and every business will have in common. At the heart of the Data team will be a Data Engineer. The Three Main Roles of a Data Engineer This is an analytics role in high demand. It is a growing and lucrative field with steps and stages for nearly every level of business and education experience. For example, a Data Scientist interested in stepping into a Data Engineer role might begin as a Generalist. In all, there are three main roles for each level and type of business – Generalist, Pipeline-Centric, and Data-Centric. Let’s take a quick look at each of the roles with an eye toward the type of person who might be the best fit: Generalist – Most often found on a small team, this type of Data Engineer is most likely the only Data-focused person in the company. They may have to do everything from build the system to analyze it, and while it carries its own unique set of skills, it doesn’t require heavy architecture knowledge as smaller companies may not yet be focusing on scale. In a nutshell, this might be a good entry point for a Data Scientist interested in upskilling and reskilling themselves to transition into a Data Engineering role.Pipeline-centric – This focus requires more in-depth knowledge working with more complex Data science needs. This type of role is found more often in mid-sized companies as they grow and incorporate a team of Data professionals to help analyze and offer actionable insight for the business. In a nutshell, this role creates a useful format for analysts to gather, collect, and analyze each bit of Data at each stage of development.Database-centric – This role is found most often in larger companies and deals not only with Data warehouses, but is focused on setting up analytics databases. Though there are some elements of the pipeline, this is more fine-tuned. In a nutshell, this role deals with many analysts across a wide distribution of databases. A Fine Balance Between Technical Skills, Soft Skills, and Business Acumen While it’s important for anyone filing this role to have deep knowledge of database design as well as a variety of programming languages, its equally important to understand company objectives. In other words, once the groundwork is laid and the datasets established, it’ll be important to explain what it is the business executives need to know to make the best decisions for their business.  Knowing how and what to communicate to executives, stakeholders, and your Data team also means understanding how to best retrieve and optimize the information for reporting. Depending on your organization’s size, you may need both a Data Analyst or Scientist and a Data Engineer. Though this is less likely in medium and larger enterprises. On the flip side, in order to understand the business’ needs, you’ll also need to be good at creating reliable pipelines, architecting systems and Data stores, and collaborating with your Data Science team to build the right solutions. Each of these skills are meant to help you understand concepts to build real-world systems no matter the size of your business. One Final Thought… Do you like to build things? Tweak systems? Take things apart and see how they work, then put them back together better and more efficient than before? Then Data Engineering might be for you. Are you a business who knows you’re ready to scale up and hire a Data professional? We have a strong candidate pool and may have just the person you need to fill your role. Are you a candidate looking for a role in big Data and analytics? We specialize in junior and senior roles. Check out our current vacancies or contact one of our recruitment consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com.

The Harnham 2019 Data & Analytics Salary Guide Has Arrived

We are thrilled to announce the launch of our 2019 Data & Analytics Salary Guide. With over 1,500 respondents across the USA, this year’s guide is our largest and most insightful yet.  Looking at your responses, it is overwhelmingly clear that the Data & Analytics industry is continuing to thrive. This has led to an incredibly active market with 72% in the US willing to leave their role for the right opportunity.  Salary expectations remain high, although we’re seeing that candidates, on average, expect 10% more than they actually achieve when moving between roles.  We’ve also seen a change in the reasons people give for leaving a position, with a lack of career progression overtaking an uncompetitive salary as the main reason for seeking a change.   There also remains plenty of room for industry improvement when looking at gender parity; the US market is only 23% female, falling to 17% in Data Engineering roles and 16% in the Data Science space.  In addition to our findings, the guide also include insights into a variety of markets and recommendations for both those hiring, and those seeking a new role.  You can download your copy of the guide here.

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