Senior Python Data Engineer - Fortune 500 Retail

Boston, Massachusetts
US$120000 - US$140000 per year + Additional Benefits

Senior Python Data Engineer - Fortune 500 Retail
Boston, Massachusetts
$120,000-140,000 annually with Additional Benefits

THE COMPANY

This Fortune 500 leader in the department retail industry is looking urgently to bring on a Senior Python Data Engineer to build out, maintain, enhance, and automate a personalization engine from scratch and additionally mentor and lead Junior Engineers on the team!

This person will be enormously impactful to the customer generation, application, and retention for the internal business and will be partnering with Data Scientists on the team to gather requirements as well as utilizing knowledge around cost initiatives to successfully build a customer focused data application with automation to best customize sales and discounts to user trends. Are you looking for growth and visibility taking initiative on a major project focusing on global-level derived results? Want to be the next leader in the data side of a Fortune 500 business? Please read below for some responsibilities and requirements for this role!

THE ROLE

As the Senior Python Data Engineer, you will be focusing specifically on gathering modelling requirements from Data Scientists, pulling data from various sources and channels, and building the entire data focused back end application required to produce customer insights in order to allow for customer growth, application, and retention by working with the following:

  • Python coding to build out the back-end personalization engine
  • AWS Redshift for cloud storage capabilities
  • Working with SQL based relational databases to integrate with cloud structure
  • Interacting closely with key players in order to understand business goals and translate these into technical features
  • Making sure data is tested; accurate and up to date
  • Using best engineering practices that are standardized

YOUR SKILLS AND EXPERIENCE

In order to hold a competitive candidacy for this role, you are someone who has:

  • A Bachelor's Degree in Computer Science, Computer Engineering, or a related field
  • Expert level commercial proficiency in Python programming
  • Strong professional experience SQL coding and working on such relational databases
  • Commercial experience utilizing AWS cloud platform and pyspark big data technology
  • Experience working in data and analytics driven environment on the back end application/software development side
  • Excellent verbal and written communication skills

THE BENEFITS

  • Base Salary ranging from $120,000-$140,000 based on skill level
  • Medical, Dental, and Vision Insurance
  • 401 K
  • Flexible PTO, WFH
  • Up to 10% bonus

HOW TO APPLY

Please register your interest by sending your résumé to Kavya Kannan via the Apply link on this page.

#ZR

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52922/KK3
Boston, Massachusetts
US$120000 - US$140000 per year + Additional Benefits
  1. Permanent
  2. Big Data

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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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