Staff Backend Software Engineer - Activations

LiveRamp, Inc.•San Francisco, CA

23d•$156,500 - $235,000

About The Position

LiveRamp is hiring a Staff Backend Software Engineer for its Activations Backend team, which operates the large-scale data processing systems behind LiveRamp’s primary activation product. These systems process more than 100,000 batch jobs per day, ranging from gigabytes to over 100 terabytes, and deliver data to hundreds of destinations. The team focuses on reliable transformation, deduplication, and movement of massive datasets across clouds and regions while meeting complex rate limits and service-level agreements. The engineer will help re-architect the Activations Backend as a cloud-forward, multi-regional, delta-first, cache-aware, and cost-efficient platform. The work combines SingleStore-backed state and delta computation, Spark/Dataproc for large batch workloads, streaming systems for event-driven and incremental delivery, and workflow engines such as Temporal or Cadence. The role includes setting technical direction for major platform areas, delivering improvements from design through rollout and maintenance, and raising performance, reliability, observability, and operational excellence. The position also involves production ownership and incident response, performance and cost optimization, collaboration with partner teams, and mentoring engineers. The engineer is expected to evaluate and adopt AI-enhanced development and operational tools, and help establish effective practices for their use.

Requirements

At least 5 years of experience writing and deploying high-quality production code in a modern language such as Java, Go, or Scala, including ownership of complex production systems.
Led the design and delivery of large-scale distributed or big data systems with demonstrable business impact.
Strong data engineering and SQL skills, including data modeling and optimization of complex queries on very large tables.
Deep experience owning end-to-end data pipelines, including ingestion, transformation, orchestration, failure handling, and observability.
Experience in a cloud environment, ideally GCP, and with containerized workloads such as Kubernetes/GKE or similar.
Ability to define and drive technical strategy, evaluate tradeoffs, align stakeholders, and deliver incremental value on multi-quarter problems.
Strong communication and collaboration skills, with the ability to influence across teams and build consensus on complex technical decisions.
Demonstrated ability to mentor and grow engineers and provide effective feedback.
Comfort with ambiguity, curiosity, and the ability to turn questions into experiments and system changes.
Experience with AI-enabled development tools, or strong interest in learning and using AI-enhanced tools and helping others use them effectively.

Nice To Haves

Experience with Google Cloud Platform services such as GCS, Dataproc, GKE, Pub/Sub, BigQuery, and IAM.
Experience with workflow orchestration systems such as Temporal, Cadence, or Airflow.
Experience with Apache Spark or Dataproc for large-scale batch processing.
Experience with data warehouses such as SingleStore, BigQuery, or Snowflake.
Experience with streaming systems such as Kafka, Redpanda, or Pub/Sub, particularly for high-volume or incremental data processing.
Experience designing multi-tenant systems that enforce rate limits, fairness, and SLAs across customers and destinations.
Background in performance and cost optimization for large-scale data workloads.
Experience in advertising, marketing, data activation, or other systems where data correctness, timeliness, and scale are critical.

Responsibilities

Lead the design and evolution of a petabyte-scale activation platform toward a delta-first, cache-aware, cost-efficient architecture.
Set end-to-end technical strategy for major Activations Backend areas, including matching and delta computation, job orchestration, and delivery pipelines.
Design and build big data pipelines using Apache Spark/Dataproc, SingleStore, Kubernetes/GKE, and streaming systems where appropriate.
Use workflow engines such as Temporal and Cadence to orchestrate complex, long-running workflows, and establish reusable engineering patterns.
Design job classification, queueing, and rate-limit-aware scheduling to support multi-tenant fairness and scalability.
Optimize performance and cost by reducing duplicate processing, improving cache hit rates, tuning cluster sizing and autoscaling, and setting and tracking SLOs.
Own critical production services, coordinate incident response and postmortems, and implement structural fixes that reduce operational load and risk.
Evaluate and adopt AI-enhanced tools for coding, design, data analysis, and operational debugging, and help define best practices.
Mentor engineers through design and code reviews, pairing, and technical guidance.
Represent Activations Backend in cross-team architecture forums and external venues.
Collaborate with partner teams to improve activation reliability, speed, and observability.
Re-architect backend processing to use modern data warehouses and replace legacy Spark-heavy flows where appropriate.
Build orchestration and scheduling that routes jobs through queues and capacity pools, respects destination rate limits and SLAs, and avoids duplicated work through configuration canonicalization and caching.