Senior software Engineer

About the role

This role delivers scalable advertising infrastructure and data platforms as part of a global technology team. You own backend systems that power audience solutions for advertisers and publishers worldwide. The work spans backend services, data pipelines, and machine learning integration. You will translate complex product requirements into robust technical implementations while ensuring high availability and performance. The position requires deep collaboration with cross-functional partners to align technical strategy with business objectives. You will mentor junior engineers and contribute to architectural decisions that shape the product roadmap. Success in this role is measured by your ability to build reliable systems that directly enable advertising revenue and customer satisfaction.

Key facts

What you'll do

Design and implement scalable, secure backend APIs and microservices that serve real-time advertising workflows.

Build and maintain data storage and processing solutions that support high-throughput, low-latency requirements.

Collaborate with software, data, and ML engineers to align architecture decisions with product needs.

Communicate with product managers and ad traders to translate business requirements into technical solutions.

Implement monitoring and alerting strategies to ensure system reliability and rapid issue resolution.

Optimize data pipelines for cost efficiency and performance across cloud environments.

Contribute to the definition and enforcement of coding standards and best practices across the engineering organization.

Evaluate new technologies and tools to improve the efficiency and scalability of the existing stack.

Participate in on-call rotations to support production services and respond to critical incidents.

Drive the execution of technical tasks from design through deployment and post-launch observation.

Lead code reviews to ensure quality, consistency, and knowledge sharing within the team.

Champion testing practices to maintain high code quality and reduce production defects.

Partner with data scientists to integrate machine learning models into production advertising systems.

Ensure all deliverables meet security, compliance, and operational requirements for global deployments.

Requirements

Bring 15+ years of experience delivering software products across backend, data, and ML stacks.

Demonstrate success building high-performance distributed systems that operate at global scale.

Hold a Bachelor's degree or equivalent in a relevant field such as Computer Science or Engineering.

Show strong problem-solving skills and enthusiasm for solving complex advertising technology challenges.

Maintain fluency with the core tech stack including Python, Scala, Java, Go, Kubernetes, AWS, GCP, Spark/PySpark, Flink, Databricks, Postgres, Redis, Aerospike, Kafka, Prometheus, Grafana, Elasticsearch, Kibana, nginx, Typescript/Node.js, Terraform, ArgoCD.

Possess experience with containerization and orchestration tools to manage resilient production services.

Have a proven track record of writing clean, maintainable, and well-documented code.

Demonstrate the ability to work effectively in a fast-paced, multicultural, and distributed team environment.

Show commitment to equal employment opportunity and inclusive collaboration practices.

Exhibit strong written and verbal communication skills to articulate technical concepts to diverse stakeholders.

Display ownership of technical decisions and the impact of those decisions on product outcomes.

Have experience collaborating with cross-functional teams including product, design, and data science.

Demonstrate the ability to mentor engineers and contribute to career growth within the engineering organization.

Practical notes

This role is remote based in Spain with equal employment opportunity policies applied globally.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Roles in advertising technology often combine backend, data, and machine learning responsibilities at scale.

Common tools in this space include distributed stream processing, cloud infrastructure, and real-time data platforms.

Performance, security, and reliability are central to user-facing advertising systems.

Cross-functional collaboration with data scientists and product teams is typical.

Continuous learning is essential given fast-moving infrastructure and AI tooling.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.