Junior DataOps Engineer at Amino Apps in Madrid, Spain 🇪🇸

Junior DataOps Engineer

Location: Madrid, Spain 🇪🇸

As a DataOps Engineer at 24i, working within the Personalization team, you will be a member of a collaborative and innovative engineering team. Reporting to the Backend Engineering Lead, you will have a hands-on role throughout the full software development lifecycle, contributing to the development and operation of the cloud-based platform that powers our personalization, recommendations, and analytics capabilities.

The role combines data engineering, backend development, and cloud infrastructure, with a particular focus on building and operating reliable data pipelines and services. You will work with large and diverse datasets, helping ensure that data is ingested, processed, monitored, and made available efficiently and reliably across the platform.
You will also work closely with teams across the wider 24i organization, helping provide the data and services required for client performance reporting, recommendation models, personalization, and analytics.

As a guide, you will have several years of experience delivering production software and data systems. We value engineers with strong foundations in data and backend engineering who are also comfortable working across engineering boundaries when required.

Key Responsibilities
You will contribute throughout the software development lifecycle, from technical design and implementation through testing, deployment, monitoring, and continuous improvement.

Data Engineering & Operations:

• Design, build, and maintain reliable data ingestion and processing pipelines supporting analytics, personalization, and recommendation use cases.
• Work with structured and semi-structured datasets across cloud-based storage and processing systems.
• Develop and maintain data orchestration workflows, ensuring that dependencies, retries, failures, and recovery are handled reliably.
• Contribute to data modelling and transformation processes that make data efficient and practical for downstream consumers.
• Help ensure the quality, consistency, and reliability of data as it moves through the platform.
• Identify and address performance, scalability, and cost issues within data processing workloads.

Backend & Platform Engineering:

• Design, develop, and maintain Python-based backend services and integrations supporting the Personalization platform.
• Quickly understand existing codebases in order to analyse behaviour, fix issues, and implement new functionality.
• Write high-quality, maintainable code that meets both functional and non-functional requirements.
• Refactor and improve existing systems while preserving expected behaviour and maintaining backwards compatibility where required.
• Design solutions based on product requirements and user stories, communicating technical approaches, effort, trade-offs, and risks with the rest of the team.

Cloud, Deployment & Observability:

• Develop and operate services and data workloads running in cloud environments, primarily AWS.
• Contribute to cloud infrastructure and deployment processes, including Infrastructure as Code and CI/CD pipelines.
• Build and improve observability across services and data pipelines, using logging, metrics, tracing, and alerting to understand system behaviour and diagnose issues.
• Contribute to the team's use of modern observability standards and tooling, such as OpenTelemetry.
• Help ensure that changes can be deployed safely and reliably across development, staging, and production environments.

Quality & Collaboration:

• Design and implement appropriate unit, integration, and functional tests to ensure that systems are fit for purpose.
• Review other team members' work and provide constructive feedback through code and design reviews.
• Diagnose and resolve issues across data pipelines, backend services, infrastructure, and customer environments.
• Identify opportunities to improve our architecture, development lifecycle, tooling, reliability, and engineering practices.
• Support less experienced team members through code reviews, technical discussions, documentation, and sharing of engineering best practices.
• Plan and prioritise your work effectively and collaborate closely with other members of the engineering team.

Client Data & Integrations:

• Work with both new and existing clients, as well as internal 24i teams, to onboard and integrate data into the Personalization platform.
• Investigate data quality, integration, and platform issues encountered during onboarding and ongoing operation.
• Support existing integrations as client requirements and platform capabilities evolve.

Skills and Requirements

Must Have:

• Strong Python development skills, including experience building production backend and/or data systems.
• Strong SQL skills and experience working with analytical or operational datasets.
• Experience designing, building, or maintaining data pipelines and data processing workflows.
• Understanding of data modelling, transformation, and data quality principles.
• Hands-on experience developing and operating systems in a cloud environment, preferably AWS.
• Experience with workflow orchestration and the operational challenges of running data pipelines reliably.
• Experience implementing and operating observability for production systems using logging, metrics, tracing, and monitoring.
• Experience designing and implementing automated tests, including unit and integration testing.
• Experience with CI/CD practices and automated software delivery.
• Strong software engineering fundamentals, including the ability to design, debug, review, refactor, and maintain production systems.

Desirable:
Experience in one or more of the following areas would be beneficial, but is not required:

• AWS serverless technologies such as Lambda, API Gateway, SQS, S3, Athena, and Glue.
• Infrastructure as Code technologies such as CloudFormation, Terraform, or AWS CDK.
• Data orchestration technologies and patterns.
• OpenTelemetry or similar observability technologies.
• Columnar data formats and analytical data platforms.
• Elasticsearch or similar search and indexing technologies.
• React and TypeScript development.
• Data Science and Machine Learning modelling techniques.
• Recommendation systems and personalization technologies.
• Analytics and Business Intelligence products such as Tableau or Power BI.
• Experience developing within an Agile framework such as Scrum or SAFe.
• AWS certifications such as SysOps Administrator, Developer, or Solutions Architect.
• TV, VOD, streaming, or media domain knowledge.

The Kind of Engineer We're Looking For

This role is primarily focused on data, backend, and platform engineering, but we work in a collaborative environment where engineers may occasionally contribute outside their primary area of expertise.

You should be comfortable working across traditional engineering boundaries when needed — whether that means investigating cloud infrastructure, improving a deployment pipeline, helping diagnose an analytics issue, supporting a data-science workflow, or occasionally contributing to frontend functionality.

We don't expect you to be an expert in all of these areas. We're looking for someone with strong foundations in data and backend engineering, combined with the curiosity and engineering mindset required to understand and improve the wider platform.