Harnham
SEARCH
Shortlist

DATA ENGINEER

San Francisco, California
US$120000 - US$140000 per annum
Apply Save job

DATA ENGINEER

SAN FRANCISCO (REMOTE)

$120,000 - $140,000

An exciting opportunity for an experienced Data Engineer to join a high performing team working in an fast moving industry and with a company that is continuing to grow its platform. If you are looking to grow as a data engineer and use your skills to be apart of a new and robust data project, this is a great job for you!

THE COMPANY

This is a company focused on connecting brands to their audiences, helping companies all around the world with their data marketing performance marketing. This Data Engineer will have a great opportunity to work collaboratively among teams to be a part of building out this redefining data project.

THE ROLE

This Data Engineer will be responsible for helping build out the data platform which will require hands on work with various forms of data.

  • Develop and execute road map for the data ingress and egress stack
  • Own the technology stack for ingesting data from dozens of platforms across hundreds of clients
  • Be apart of a collaborative team to create engineering solutions
  • Develop systems to monitor data quality

YOUR SKILLS & EXPERIENCE

  • Experience working with data stack components in the cloud, such as data collection platforms, marketing platform APIs, and cloud computing
  • Proven commercial experience with Python
  • Comfortable working in an agile environment within a cross-functional and globally distributed team
  • Commercial experience with managing data infrastructure

BENEFITS

  • 120,000- 140,000 base salary
  • Remote Work
  • Medical, Vision, Dental

HOW TO APPLY

Please register your interest by sending your resume to Alexandra Oechsle via the apply link on this page.

Apply Save job
Send similar jobs by email
3242
San Francisco, California
US$120000 - US$140000 per annum
  1. Permanent
  2. Big Data

Similar Jobs

SENIOR BIG DATA ENGINEER
Salary

US$182887 - US$207272 per annum

Location

San Francisco, California

Description

This Senior Big Data Engineer will have a great opportunity to work collaboratively amongst teams and be a key contributor in building this data platform.

ADD TO SHORTLIST
Senior Data Engineer
Salary

US$130000 - US$140000 per annum

Location

Cincinnati, Ohio

Description

Looking for Senior Data Engineers to design/build data pipelines in a multi-cloud environment for data science teams.

ADD TO SHORTLIST
Data Engineer - AdTech
Salary

US$100000 - US$130000 per annum + Base Salary

Location

Maryland

Description

A Leading Ad-Tech Company is looking for bright, self-motivated data engineers to join them (can be fully remote)! Read below to learn more!

ADD TO SHORTLIST
Senior Data Engineer - CPG eCommerce
Salary

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

Location

Cincinnati, Ohio

Description

A CPG business in Cincinnati are looking for passionate and energetic seasoned senior data engineers to join their business!

ADD TO SHORTLIST

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.

Amped Up Analytics: Google Analytics 4

Google Analytics 4 has amped up data insights into the behaviors and preferences of your customers. Where once each touchpoint only tracked what had been clicked, GA4 is bringing it all together in a more wholistic approach to the customer journey. As the fourth quarter of 2020 dawned, Google upped its game. Crafting a compelling array of features with machine learning at its core, this new platform offers a more customer-centric approach to data-driven insights, rather than split data across platforms and devices.   Though still in its infancy, there are some dramatic new changes afoot. And while it’s not a good idea to get rid of the old Universal Analytics platform before ringing in the new one, it is a good idea to understand what’s available now and what may come to be over time. Four Advantages to Google Analytics 4.0 From our desktop to our laptop to our smartphone, we carry our office in our pocket or on our lap. So, what better way to integrate what was once called “App + Web properties” into a more cohesive trackable measurement of data. Add to this the privacy protocols in place to protect customers, and Google Analytics 4 offers flexibility for future cookieless tracking and permissions, and advantages are revealed. 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.  The platform’s premise is that there is a pattern everyone follows. From the moment a customer visits your website to clicks on a button subscribing to your newsletter or blog – Acquisition and Engagement. To the moment your customer makes a purchase, is happy with the product or sevice, and comes back again – Monetization and Retention.  Designed for marketers who want to track users across multiple formats, Google Analytics 4 hopes to solve with Data Streams. These Data Streams merge to paint a picture of the customer journey from website visit to purchase. A Focus on Anonymized Data This anonymization answers the call to Data Privacy and third-party data collection. Crafting a unified user journey centered 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. This is a key change in that Google is moving away from client-side focus and using server-side and customer-centric capabilities. With GDPR and privacy laws in full swing, marketers face enhanced privacy regulations as cookies are phased out or blocked. Predictive Metrics and Audiences 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.  Though focused on e-commerce sites now and based on transactions and revenue, there is an opportunity for marketers to identify and convert based on such leads as video views or form submissions. Machine Learning-Driven Insights The launch announcement for 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.  What These Changes Mean on the Digital Frontier We’re all reaching for higher value and Google Analytics 4.0 brings it into one unified platform for the future. As we make the shift from traditional Google Analytics to its 4.0 version, there is opportunity to get more creative.   Wondering if you should upgrade? This article breaks down the pros and cons to help you decide.  If you’re interested in Big Data & Analytics, Harnham may have a role for you. Check out our current vacancies or contact one of our expert consultants to learn more.  For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.  For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Joshua Poore

Joshua Poore

08. April 2021

News

Making Sense of Unstructured Data with NLP

Natural Language Processing. It seems a simple enough explanation. The idea is to make computers sound like native speaking humans regardless of their language. Except there’s one problem. When we speak, we don’t follow our own rules of grammar. We use idioms, metaphors, abbreviations, and oftentimes use more body language to communicate than we realize.  So, what’s a poor machine to do when confronted with such an unstructured melee of data? Well, since semantics is not what you say it’s how you say it, we must teach computers to read between the lines. Of code. Enter NLP. The semantics of human language written for a machine to help make sense of our human behaviors gets organized. The Perfect Imperfections of Language Computers require structure. Natural language does not. Teaching machines how we communicate is no easy task, and yet we use machines that can do this every day. By combining technology and Machine Learning we begin to teach computers how to understand us. We teach them how to interpret and determine what it was we want done. When you’re asking Siri or Alexa a question, you’re helping them to learn how you ask, so they can better respond, and they make you more efficient. It’s a win-win for everyone. In business, using NLP techniques to drive business decisions is even more important. Now, the computer must decide what information is the most valuable to pull from a pile of Data. Understanding our choices, our tone, even the words we choose to use, helps our machines learn what we want to do or need done. Where is NLP Used? Since we use different rules when we speak than when we write, our computers learn how we talk and how to use language more naturally. Wondering where NLP might be used? In a word or two? Nearly everywhere. You are scheduling a meeting and when it’s time, a calendar reminder pops into your phone which says estimated drive time to the meeting based on traffic conditions in your area. Or you ask Alexa to play your favorite music list from Pandora.  Every touchpoint in this scenario is using NLP. We naturally might get into our car, ask our Virtual Assistant navigation system for directions, or to play our favorite music. Our choices don’t fit in a box and may not be logical, but the more we teach the machines, the closer they may get to understanding the nuances of our language. Here are 5 more ways we use NLP every day: Predictive text on your phone or in your Word document. Chatbots and Virtual Assistants to ensure customers are acknowledged in a timely manner, answer basic questions or redirect to appropriate personnel, and making suggestions to improve the customer experience.Curating social media feeds to determine customer needs and interest.Grammar correction software so our emails and business documents are error-free.Analyzing customer interactions using comments and reviews for customer feedback about a product or service. There’s a ton of information to be filtered, sorted, sifted, and analyzed, and NLP is just one of the tools Data Scientists use. Interested in the subfield of NLP? Check out this article for 6 techniques you need to know to get started. Already well-versed in the industry and looking for a new challenge? If you’re interested in Big Data and Analytics, Advanced Analytics, Life Sciences, Data Science, or any of our Data professional fields, we may have a role for you. Review our current vacancies or contact one of our expert consultants to learn more.   For our West Coast Team, contact us at (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast teams contact us at (212) 796-6070 or send an email to newyorkinfo@harnham.com.  

Elizabeth Sobel

Elizabeth Sobel

25. March 2021

News

Recently Viewed jobs