Data Engineer

About this role

<div class="content-intro"><p><span class="NormalTextRun SCXW30632256 BCX0">Resonate is a leading provider of high-quality, AI-powered consumer data, intelligence, and technology, empowering marketers to create a more personalized world that increases customer acquisition and lifetime value. Our SaaS platform, Ignite, and our Data-as-a-Service (DaaS) offerings </span><span class="NormalTextRun SCXW30632256 BCX0">provide</span><span class="NormalTextRun SCXW30632256 BCX0"> unparalleled insights into consumer motivations, values, and behaviors, enabling our clients to connect with their target audiences in more meaningful and effective ways. We are a dynamic and fast-growing company </span><span class="NormalTextRun SCXW30632256 BCX0">seeking</span><span class="NormalTextRun SCXW30632256 BCX0"> passionate and innovative individuals to join our team!</span></p></div><p>Plenty of data engineering roles are about keeping the pipeline alive. This one is about building the thing the whole business runs on.</p> <p>At Resonate, we help enterprise brands understand not just who their customers are, but why they behave the way they do. That intelligence is only as good as the pipelines underneath it, and those pipelines move terabytes at a time. You will be on the big data team designing, building, and maintaining them - hands-on Spark and Scala work on AWS, at a scale where the usual answers stop working and you have to think properly about the problem.</p> <p>It is also a team that has genuinely committed to Gen AI as part of how the work gets done, not as a pilot someone runs on the side. If you want to build at real scale and have room to change how the building happens, this is the role.</p> <p>&nbsp;</p> <p><strong>What you’ll be doing</strong></p> <ul> <li>Designing, developing, and maintaining the ETL/ELT pipelines that power the Resonate business - Spark and Scala on AWS EMR, working with terabyte-scale datasets across S3 and Snowflake</li> <li>Tuning multi-terabyte and petabyte scale Spark applications for performance and cost, and debugging the problems that only ever show up at that size</li> <li>Partnering with senior engineers and product management to take product requests from design and planning through to pipelines running in production</li> <li>Monitoring pipeline health in Grafana, tracking down data quality issues, and fixing the cause rather than the symptom</li> <li>Writing clean, testable code with thorough unit and integration tests, using Gen AI to move faster through the parts that used to slow you down</li> <li>Using Gen AI tools across pipeline development, monitoring, and day-to-day engineering work - and helping the team figure out where they genuinely help and where they do not</li> <li>Taking part in code reviews, technical design discussions, and sprint planning, with your point of view on architecture actually counting</li> <li>Supporting production operations, including an on-call rotation and incident response</li> </ul> <p>&nbsp;</p> <p><strong>What we’re looking for</strong></p> <p><strong>You’ll need</strong></p> <ul> <li>Around five years or more of professional experience in software engineering, data engineering, or a closely related field</li> <li>At least three years hands-on with Spark and Scala, specifically the DataFrame and Dataset APIs</li> <li>Proven experience tuning Spark applications at multi-terabyte or petabyte scale - you have done it, not just read about it</li> <li>Real debugging and problem-solving experience in the big data ecosystem, including the jobs that only fail in production</li> <li>Solid relational database experience</li> <li>Working knowledge of modern cloud stacks for processing big data - AWS (EMR, S3, Lambda), Kafka, Snowflake, Grafana, Hadoop, Elastic Stack, and Docker</li> <li>A strong grasp of the full software development lifecycle, from exploration and design through to delivery in production, plus good instincts on solution architecture, data structures, and data modeling</li> <li>Real enthusiasm for using generative AI to accelerate data engineering work - pipeline development, system monitoring, and developer productivity</li> </ul> <p><strong>Strong signals</strong></p> <ul> <li>Hands-on experience building and maintaining big data pipelines with Gen AI</li> <li>Experience using probabilistic data structures</li> <li>A track record of implementing high cardinality data systems at scale</li> <li>A bachelor’s degree in computer science, computer engineering, or equivalent experience</li> </ul> <p>&nbsp;</p> <p><strong>Who does well here</strong></p> <p>You care about the quality of what you ship and you are happy to say so in a design discussion. You are the kind of engineer who wants to know why a job got slower, not just make it faster again, and you would rather write the test than explain later why there was not one. You are comfortable being hands-on and self-directed, and you do not need someone to hand you a fully specified ticket before you can make progress.</p> <p>You are also curious about how the work itself is changing. The engineers who do well here treat Gen AI as something to get good at, not something to be wary of, and they bring the rest of the team along with what they learn. It is a small, friendly team where what you build is visible and matters to the business straight away.</p> <p>&nbsp;</p> <p><strong>Why Resonate</strong></p> <ul> <li>Data at a scale that is genuinely difficult - terabyte and petabyte workloads, high cardinality, and problems that need real engineering rather than a bigger cluster</li> <li>A team that has already made the shift to Gen AI in day-to-day engineering work, with the space to push that further</li> <li>A modern stack you will actually work across, not one you will hear about in a roadmap deck: Spark, Scala, AWS, Snowflake, Kafka, Docker</li> <li>A company past the early-stage chaos, with serious enterprise customers, a clear direction, and enough scale to make the