Senior Big Data Engineer - CPG eCommerce

Cincinnati, Ohio
US$120000 - US$130000 per annum + Additional Benefits

Senior Big Data Engineer - CPG eCommerce

$120,000 - $130,000 base annual salary with additional benefits

Cincinnati, OH

THE COMPANY

This multi-billion-dollar industry conglomerate is looking to bring in intelligent, passionate, top notch cloud-based big data engineers to join their innovative and expansive brand to help grow their R&D Analytics branch to inform various major business decisions.

If you are a forward-thinking self-starter looking to embark on a new career with one of the largest world-renowned businesses, this could be the next opportunity for you!

THE ROLE

As Senior Big Data Engineer, your skillset will be highly utilized by the data science team to help derive insights on various product and service offerings such as consumer behavior and mark downs etc. Specifically, you will be responsible for the following key tasks:

  • Gathering requirements and collaborating closely with the Data Scientists to translate into technical output
  • Knowing which questions will help to resolve which used case situations
  • Coding in Python and Spark to build data pipelines and back end ML support functions
  • Working in a cloud hybrid environment of Azure, GCP and migrating existing data into AWS

YOUR SKILLS AND EXPERIENCE

In order to be considered for the Senior Big Data Engineer position, you must have at minimum the following prerequisites:

  • A Bachelor's or higher degree in Computer Science/Engineering/related field
  • Seasoned commercial data engineering experience
  • A willingness to learn new technologies and tools and a passion for code
  • Strong previous professional collaboration experience with data science teams
  • Prior hands-on commercial experience with Python and Spark
  • Prior hands-on commercial experience with at least one of the following cloud services, preferably serverless: AWS, GCP, Azure

THE BENEFITS

  • $120,000 - $130,000 base annual salary depending on experience level
  • Additional annual bonus package paid out at the end of the year
  • Medical, Dental, Vision Insurance
  • Competitive PTO and STO packages
  • Relocation (as needed)
  • Higher education resources
  • An energetic, intelligent, and forward-thinking environment

***Unfortunately, the client is not able to offer sponsorship or transfer of sponsorship for those who need it either now or in the future.***

HOW TO APPLY

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

KEYWORDS

Big Data, Data Engineering, Data Pipeline, Data Architecture, Design, Python, AWS, Real Time, Streaming, Batch Processing, ETL, ELT, Data Formatting, Data Structure, Data Modeling, Data Governance, Data Cleansing, ML, Deep learning, NLP, PyTorch, Petabytes, R&D, Data Science

Send similar jobs by email
98504/KK34
Cincinnati, Ohio
US$120000 - US$130000 per annum + Additional Benefits
  1. Permanent
  2. Big Data

Similar Jobs

Salary

£60000 - £70000 per annum

Location

London

Description

An opportunity for a mid-senior level Data Engineer to join a large corporation.

Salary

US$120000 - US$130000 per annum + Additional Benefits

Location

Cincinnati, Ohio

Description

My client in Ohio are looking for big data engineering experts looking to join a learning-based cutting edge environment to grow technically!

Salary

US$150000 - US$160000 per annum

Location

New York

Description

Looking for a Salesforce Developer to join a Data Intelligence consulting firm that provides innovative solutions to meet business goals.

Salary

£75000 - £85000 per annum

Location

London

Description

I am now recruiting for a Senior Data Engineer with experience in client facing positions, mentoring and leadership experience and with a broad tech stack.

Harnham blog & news

With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

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

Weekly News Digest: 4th-8th Jan 2021

Happy New Year! This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of data and analytics.  TechRepublic: How IT can prepare for the coming hybrid work environment As the world continues to feel the pressures of COVID-19 , remote working is no longer the temporary and novel approach to work that we had envisaged. Vaccines are being approved and healthcare professionals are supporting its rollout across the globe. And, as each dose is administered, we move one step closer to what is likely to become a hybrid working situation. It is therefore pressing for tech leaders to prepare for a shift to this style of work. TechRepublic have explored how these leaders need to ensure that their technology is agile enough to support the needs of the workforce. Yet they also need to look beyond the tech, to redefine how teams work together. Read the full article here. Forbes: 350 CMOs: 3 Marketing Supertrends For 2021 ... And The No-Hype Future Of Marketing Tech We’re a big fan of this piece from John Koetsier, writing for Forbes. He describes how the marketing trends of the year ahead will take a focus on the holistic transformation in a digital-first world. Drawing on the thoughts of a range of Chief Marketing Officers, Koetsier explores that a mixture of new, emerging technologies will see the evolution of marketing to put digital right at the core. Openpath CMO Kieran Hannon, “Now meaningful customer-centric digital transformation can accelerate.” Suzanne Kounkel, Chief Marketing Officer for Deloitte, “Fusion is the new ecosystem. Fusion is the art of bringing together new business partnerships, customer insights, and digital platforms to create ecosystems.” Tristan Dion Chen, CMO of University Credit Union, “It is without a doubt crucial to recognize how COVID-19 has ushered in a strong sense of empathy as a driving force within the marketing industry.” The marketing industry is set to experience continued innovation and growth. Read more on this here. ZDNet: Facial recognition: Now algorithms can see through face masks Last year was a year unlike any other. The complete shift in the way we have had to go about our day-to-day lives, brought about by the ongoing implications of the COVID-19, is still being felt now. One of these changes to our lives is the compulsory requirement to wear a face mask when leaving home. Now, of course, this requirement has brought up some challenges for using our technology, such as banking and payment applications, which need facial recognition to activate it! However, ZDNet have reported that algorithms can now see-through face masks (pretty sweet, right?) The US Department of Homeland Security has carried out trials to test whether facial recognition algorithms could correctly identify masked individuals. This could be a real support for travel, banking and mobile technology in the future. Read more on the trial here.  Towards Data Science: Predicting the outcome of NBA games with Machine Learning The NBA season is back and well underway. Will the Los Angeles Lakers take the top spot again this year? Lots of fans will be making their own predictions as the season begins, but new research has been used to help predict the outcome of NBA games – with the help of the insightful tech that is machine learning. Focusing on five core steps, the team at ‘Towards Data Science’ used Big Data Analytics to help them predict the outcome of games: Scraping Relevant DataCleaning and Processing the DataFeature EngineeringData AnalysisPredictions Through the research, they found that the best published model had a prediction accuracy of 74.1 per cent (for playoff outcomes), with most others achieving an upper bound between 66–72 per cent accuracy. That’s scarily good! Click here to read more on the study and see the statistics in action. We've loved seeing all the news from Data and Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.

The Search For Toilet Paper: A Q&A With The Data Society

We recently spoke Nisha Iyer, Head of Data Science, and Nupur Neti, a Data Scientist from Data Society.  Founded in 2014, Data Society consult and offer tailored Data Science training for businesses and organisations across the US. With an adaptable back-end model, they create training programs that are not only tailored when it comes to content, but also incorporate a company’s own Data to create real-life situations to work with.  However, recently they’ve been looking into another area: toilet paper.  Following mass, ill-informed, stock-piling as countries began to go into lockdown, toilet paper became one of a number of items that were suddenly unavailable. And, with a global pandemic declared, Data Society were one of a number of Data Science organisations who were looking to help anyway they could.  “When this Pandemic hit, we began thinking how could we help?” says Iyer. “There’s a lot of ways Data Scientists could get involved with this but our first thought was about how people were freaking out about toilet paper. That was the base of how we started, as kind of a joke. But then we realised we already had an app in place that could help.” The app in question began life as a project for the World Central Kitchen (WCK), a non-profit who help support communities after natural disasters occur.  With the need to go out and get nutritionally viable supplies upon arriving at a new location, WCK teams needed to know which local grocery stores had the most stock available.  “We were working with World Central Kitchen as a side project. What we built was an app that supposed to help locate resources during disasters. So we already had the base done.” The app in question allows the user to select their location and the products they are after. It then provides information on where you can get each item, and what their nutritional values are, with the aim of improving turnaround time for volunteers.  One of the original Data Scientists, Nupur Neti, explained how they built the platform: “We used a combination of R and Python to build the back-end processing and R Shiny to build the web application. We also included Google APIs that took your location and could find the closest store to you. Then, once you have the product and the sizes, we had an internal ranking algorithm which could rank the products selected based on optimisation, originally were based on nutritional value.”  The team figured that the same technology could help in the current situation, ranking based on stock levels rather than nutritional value. With an updated app, Iyer notes “People won’t have to go miles and stand in lines where they are not socially distancing. They’ll know to visit a local grocery store that does have what they need in stock, that they’ve probably not even thought of before.” However, creating an updated version presented its own challenges. Whereas the WCK app utilised static Data, this version has to rely on real-time Data. Unfortunately this isn’t as easy to come by, as Iyer knows too well:  “When we were building this for the nutrition app we reached out to groceries stores and got some responses for static Data. Now, we know there is real-time Data on stock levels because they’re scanning products in and out. Where is that inventory though? We don’t know.” After putting an article out asking for help finding live Data, crowdsourcing app OurStreets got in touch. They, like Data Society, were looking to help people find groceries in short supply. But, with a robust front and back-end in place, the app already live, and submissions flying in across the States, they were looking for a Data Science team who could make something of their findings.  “We have the opportunity,” says Iyer “to take the conceptual ideas behind our app and work with OurStreets robust framework to create a tool that could be used nationwide.” Before visiting a store, app users select what they are looking for. This allows them to check off what the store has against their expectations, as well as uploading a picture of what is available. They can also report on whether the store is effectively practising social distancing. Neti explains, that this Data holds lots of possibilities for their Data Science team: “Once we take their Data, our system will clean any submitted text using NLP and utilise image recognition on submitted pictures using Deep Learning. This quality Data, paired with the Social Distancing information, will allow us to gain better insights into how and what people are shopping for. We’ll then be able to look at trends, see what people are shopping for and where. Ultimately, it will also allow us to make recommendations as to where people should then go if they are looking for a product.”  In addition to crowdsourced information, Data Society are still keen to get their hands on any real-time Data that supermarkets have to offer. If you know where they could get their hands on it, you can get in touch with their team.  Outside of their current projects, Iyer remains optimistic for the world when it emerges from the current situation: “Things will return to normal. As dark a time as this is, I think it’s going to exemplify why people need to use Artificial Intelligence and Data Science more. If this type of app is publicised during the Coronavirus, maybe more people will understand the power of what Data and Data Science can do and more companies that are slow adaptors will see this and see how it could be helpful to their industry.”   If you want to make the world a better place using Data, we may have a role for you, including a number of remote opportunities. Or, if you’re looking to expand and build out your team with the best minds in Data, get in touch with one of expert consultants who will be able to advise on the best remote and long-term processes. 

Recently Viewed jobs