Data Engineer

Charlotte, North Carolina
US$170000 - US$180000 per annum

Data Engineer
Private Equity
Charlotte, NC
$170,000 - 180,000

THE COMPANY:

A top private equity firm that leads in algorithmic trading in the real estate space with assets under management of around $13 billion. They are one of the top providers in single-family rental homes, with over 41,000 homes in the fastest-growing metro markets in the U.S. They are committed to doubling their portfolio size in the next year.

THE ROLE - Data Engineer
As a Senior Data Engineer, you will develop data pipelines, ingrate 3rd party applications, and work closing with data science teams performing statistical analysis using Python. You will update/design the cloud data platform to handle the new real estate data coming in for the data science teams to perform statistical analysis. Your responsibilities will include:

  • Design and optimize data pipelines
  • Design and optimize cloud-based data warehouse solutions for SQL and NoSQL data sources
  • Design solutions that are highly scalability
  • Work with application and data science teams
  • Be able to present to technical and non-technical stakeholders

YOU WILL NEED:

  • Commercial experience with SQL and NO SQL databases
  • Expert at integrating 3rd party APIs into a cloud-based data warehouse
  • Expert in one or more cloud services like AWS or Azure
  • Experience with Snowflake
  • Expert with Python
  • Experience with Juptyer notebooks is a plus

THE BENEFITS:

  • $170,000 - $180,000 base
  • Annual bonus
  • Health benefits
  • 401K
  • PTO and sick time off

HOW TO APPLY

Please register your interest by sending your resume to Jacob Ragland via the Apply link on this page.

KEYWORDS

Data Engineer, AWS, Azure, Snowflake, Python, NoSQL, SQL, Juptyer notebook, data warehouse, ETL, ELT, cloud, MongoDB, S3, real estate, teamwork

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Charlotte, North Carolina
US$170000 - US$180000 per annum
  1. Permanent
  2. Big Data

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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: 5th - 9th April 2021

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 & Analytics.    The Drum: How data visualisation turns marketing metrics into business intelligence Gathering data is just one part of a marketer’s job but having the ability to turn this data into something visually stunning, informative and easy to use is another skill completely.  Marketers, on the whole, are extremely visual learners along with around 65 per cent of the population. Most of us are able to absorb data more effectively if the information being presented to us is done in such a way that is pleasing to the eye. And this is why Data Visualisation exists; it allows us to group, organise and represent data sets in a way that allows us to analyse larger quantities of information, compare findings, spot patterns and extract meaningful insights from raw data. Not only does Data Visualisation allow us to learn more effectively, but we can then turn this understanding into much broader and deeper Business Intelligence.  To read more on the positives of Data Visualisation and how to translate this into meaningful Business Intelligence, click here.  ZDNet: The five Vs of customer data platforms According to ZDNet, Customer Data Platforms (CDPs) are the hottest marketing technology today, offering companies a way to capture, unify, activate, and analyse customer data. Research done in 2020 by Salesforce showed that CDPs were among the highest priority investments for CMOs in 2021. If you’re planning to invest in a CDP this year, what five critical things do you need to think about when developing a successful strategy? ZDNet tells all.  Velocity - Your systems need to manage a high volume of data, coming in at various speeds.Variety - Every system has a slightly different main identifier or "source of truth," and the goal is to have one. This starts with being able to provision a universal information model, or schema, which can organize all of the differently labelled data into a common taxonomy. Veracity - Companies must ensure they can provision a single, persistent profile for every customer or account.Volume - It has been theorized that, in 2020, 1.7MB of data was created every second for every person on Earth. If you want to use those interactions to form the basis of your digital engagement strategy, you have to store them somewhere. Value - Once you have a clean, unified set of scaled data – now’s the time to think about how to derive value from it.  To learn more, read the full article here. Towards Data Science: How to Prepare for Business Case Interview Questions as a Data Scientist When you think of Data Science, the first thing that comes to mind will be technical knowledge of coding languages and fantastic statistical ability; softer skills such as communication and exceptional business knowledge may be overlooked. However, this is where many budding Data Scientists trip up. It is these softer skills and business acumen that sets brilliant candidates apart from others.  But how, when not usually taught at university, do you gather the business knowledge that will set you apart from the competition and showcase it in interview? Towards Data Science shares a few key pointers. Build a foundation – Brush up on your business basics. Research project management methodologies, organisational roles, tools, tech and metrics - all are crucial here. Company specifics – Research your company and its staff. Make sure your knowledge is tailored to the company you’re interviewing for. Products – This is where you’ll stand out above the rest if you get it right. The more you can know the ins and outs of products and metrics at the company, the more prepared you will be to answer business case questions. Read the full article here.  Harnham: Amped up Analytics: Google Analytics 4 Joshua Poore, one of our Senior Managers based in the US West division of Harnham, explores Google’s new and improved data insight capabilities, predominantly across consumer behaviours and preferences.  This exciting new feature of Google was born in the last quarter of 2020 and has now fully come into its infancy, and it’s an exciting time for Data & Analytics specialists across the globe. Joshua explores four key advantages of Google Analytics 4.0. Combined data and reporting - Rather than focusing on one property (web or app) at a time, this platform allows marketers to track a customer’s journey more holistically. A focus on anonymised data - By crafting a unified user journey centred around machine learning to fill in any gaps, marketers and businesses have a way to get the information they need without diving into personal data issues.Predictive metrics - Using Machine Learning to predict future transactions is a game changer for the platform. These predictive metrics for e-commerce sites on Google properties allow for targeted ads to visitors who seem most likely to make a purchase within one week of visiting the site. Machine Learning driven insights - GA4 explains it “has machine learning at its core to automatically surface helpful insights and gives you a complete understanding of your customers across devices and platforms.” Machine Learning-driven insights include details that elude human analysts.  To read Joshua’s full insights on GA4, click here.  We've loved seeing all the news from Data & 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. 

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