LiveRamp's product is a massive identity graph connecting each individual to their online identifiers like cookies and device IDs.
Our engineering workflows can be broken into three parts: Data Ingestion, Data Manipulation, and Data Distribution. We ingest behavioral data indexed by offline and online identifiers from our clients, resolve every index to a single LiveRamp ID through our Identity Graph, and then distribute ingested data to other technology platforms in the industry.
Engineering Manager, Big Data Infrastructure San Francisco
Lead UI Engineer New York or San Francisco
Principal Software Engineer - Backend San Francisco
Senior Software Engineer, Big Data (Data Management) San Francisco
Senior Software Engineer, Data Science San Francisco
Senior Software Engineer, DevOps San Francisco
Senior Software Engineer, Full Stack (Data Management) San Francisco
Senior Software- Full Stack (Activations) San Francisco
Software Engineer, Backend (Integrations) New York or San Francisco
Software Engineer, Backend (Massive Graph Processing) San Francisco
Sr. Full Stack Engineer, Customer Profiles San Francisco
Sr. Full Stack Engineer, Customer Profiles - (Lines of Business) San Francisco
LiveRamp was acquired by data giant Acxiom in 2014. In 2018, Acxiom sold the non-LiveRamp pieces of the business for ~$2B in cash and recently debuted on the New York Stock Exchange (ticker: RAMP) as an independent public company. Our fundamentals are exceptional: 30%+ annual revenue growth, $1.7B in cash, no debt, and over 600 customers. And we're growing our team at a record pace.
We work collaboratively to solve big and interesting problems. We use an agile framework of development that entrusts and empowers each engineer with the autonomy and support they need to solve big challenges.
We work with a 2400+ node Hadoop cluster, with more than 90,000 CPUs and raw HDFS capacity in excess of 95 petabytes. Our Identity Graph has hundreds of billions of nodes, using modified Pregel and Giraph algorithms for traversal . We regularly push the limits of big data processing tools with our scale. This year, our engineering team completed our migration to Google Cloud Platform (GCP), a major change to the way we work today that will set us up for greater success in the future.
We break our engineering team into six
Platoons. Each platoon has a number of
Squads (scrum teams) consisting of 5-7 individuals. These squads are the elemental unit of production at LiveRamp, consisting of developers, tech leads, and product managers. In general we employ an agile framework of organization, with 2-week sprints being the norm. However, each squad has its own flavor of development, and we encourage and support squads to do what they need to do to get things done.
Our applications layer of development faces the challenge of distilling our highly complex product into digestible components for our clients to use. Our full-stack team is also responsible for building clean interfaces between our internal services.
Our backend teams regularly deal with scaling issues that arise from processing data of enormous size —building databases for efficient querying and manipulation of data, building our identity graph through pixel-serving, making that graph efficient and organized through custom algorithm design, etc.
Our infrastructure team is currently focused on developing tools for simple containerization and servicization of our codebase. They are also responsible for optimizing and supporting our 2400+ node, 95 petabyte hadoop cluster, eventually moving this from a physical colocation center to GCP leveraging Kubernetes.
Sample past project: Our ingestion pipeline is a monolithic service and is prone to silent failures, oftentimes requiring jobs to be restarted for unknown reasons. Refactor the ingestion pipeline to allow for file-based tracking for viewability and diagnosability, recoverability from errors and failures, ad hoc prioritization , and intelligent traffic shaping for efficiency.
Real-time data collection from billions of requests a day (peak 200K QPS), generate linkages for our massive identity graph, enable access to linkages in our identity graph in real-time.
Pregel Path Computer: This system finds relevant graph paths using the pregel graph computation framework as implemented in Apache Giraph. There are challenges in running Giraph at the scale of our graph and we’re constantly looking to refine our Pregel algorithms.
Edge Ingestion and Partitioning Framework: We could never process all trillion edges at once and luckily we don’t have to. Instead we process subgraphs that contain specific types of edges. Our edge ingestion and partitioning framework manages different Hadoop datastores for different types of edges and automates the ingestion of new edge data. It leverages LiveRamp’s Seek MSJ framework to efficiently incorporate new data into existing edge stores.
Path Computation as a Service: We provide a service to other LiveRamp engineering teams for finding specific types of paths within our massive graph. It handles 20,000 requests a day and this is possible due to its use of caching and intelligently batching similar request together.
All LiveRampers are smart, nice, and get things done. We move quickly and value autonomy and project ownership over all else. We empower our people to use their best judgment when making decisions. We grow our talent by challenging them with stretch projects and supporting them with mentorship.
Unlimited paid time off.
Catering & Snacks.
$100 per month.
SF: Downtown in the Financial District, 2 minutes from Montgomery Street Station.
NY: Right next to Union Square/Flatiron District
We have both dogs and puppies.
We have an annual, company-wide camping trip called RampCamp. We spend the weekend playing board games, dancing, and relaxing over food & drink.
If you want to go, we'll pay for it. If you want to present, we'll support you as much as we can.
Included in unlimited paid time off. Take a few months off to care for your baby.
Board game nights, trivia nights, off-sites, fancy dinners, and quarterly company-wide events.
Dollar for dollar cash match up to 6% of your salary.
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