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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Manychat - **Location:** Barcelona, Spain (Remote available) - **Salary:** €65,000.0 - €83,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Big Data, Cloud Database, Code Review, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Data Validation, Information Engineering, Data Infrastructure, Data Warehousing, Software Debugging, Python (Programming Language), Machine Learning, Operational Databases, RabbitMQ, Standard Sql, Software Engineering, Data Ingestion, Apache Spark, Data Lakes, Kubernetes, Apache Kafka, Machine Learning Operations, Terraform, Data Pipelines, Docker - **Published:** August 12, 2026 - **Apply:** https://www.adzuna.es/contact-us.html ## About the Role + Several years of experience building and operating production data platforms - data warehousing, data lakes, orchestration, and ingestion are the bread and butter of your experience, including events streaming + Strong Python and SQL skills with solid software engineering practices (testing, CI/CD, code review - you treat data code as code) + Deep experience with an orchestrator (we use Airflow): not just writing DAGs, but running, scaling, and debugging the thing itself + Strong experience with cloud platforms (we use AWS) and cloud data warehouses + Working knowledge of Docker and Kubernetes - you can deploy, inspect, and debug your own workloads + You've seen enough incidents to design for failure: idempotency, backfills, data quality checks, and monitoring are reflexes, not afterthoughts + You communicate like a senior: you can explain a technical trade-off to an analyst, a PM, or a fellow engineer - and you write things down properly + You're proactive and pragmatic: you don't just solve the problem, you ask why it happened, whether it's worth preventing, and what the simplest robust fix is + An eye on the future: you're excited by AI tools automating operational work, and you want to help us build toward that, + You must have very good English, communication skills, both spoken and written ## Description You'll be a technical reference point in the team: designing and building the systems that ingest, transform, and serve data for the whole company - and raising the bar on how we do it. That means owning architecture decisions, driving them from proposal to production, and mentoring other engineers along the way. Our platform spans ingestion from dozens of heterogeneous sources, orchestration, a cloud data warehouse, transformation layers, and the infrastructure underneath it all. You won't just work on one slice: we expect seniors to be comfortable across the stack - from a dbt model that analysts depend on, to the Kubernetes workload that runs it, to the Terraform that provisions it, to the orchestration of all the pipelines involved. You'll go deeper in some areas than others, and we'll make sure your depth is put to good use. This is a hands-on engineering role with real ownership: you'll ship, operate what you ship, and shape the roadmap of the platform itself. What you'll be doing + Design, build, and operate scalable data ingestion and processing pipelines across many source systems + Lead architectural decisions on the platform and write the proposals that get them adopted + Improve the reliability, observability, and cost-efficiency of the platform: if it pages someone at 9am, you'll want to know why and make it not happen again + Develop our infrastructure in close cooperation with Platform engineers + Work alongside analytics engineers, analysts and all data consumers to make the data experience more humane - better contracts, better tooling, better self-service + Mentor other data engineers through code review, pairing, and design feedback + Drive automation and AI-assisted operations: we'd rather build the tool than solve the same problem twice + Interact daily with other teams (analytics engineers, analysts, developers, platform engineers, product managers) to turn ambiguous data problems into shipped solutions, + More platform-leaning: Kubernetes operations, Infrastructure as Code (Terraform), Docker optimization, and CI/CD; building and running the runtime layer that data workloads live on + More enablement-leaning: enabling different product areas in using the data platform, including ingestion, modeling through dbt and serving data to different scopes + In both directions: experience with streaming and message-driven architectures. We work with RabbitMQ, so that's a strong plus, but Kafka or similar counts too A bonus either way: big data tooling (Spark) or experience operationalizing ML models. We don't expect one person to be deep in all of the above: tell us where your spike is. Whichever direction you come from, the job is data engineering across the stack. Why join Docplanner's Data Platform Team? + You'll be part of a tight-knit, cross-functional team of data engineers, analytics engineers and ML Ops engineers + We treat the data platform as a product - you'll help build and improve it continuously, and as a senior you'll help decide where it goes next + Real scale, real impact: our platform powers decision-making for a company serving 90+ million patients a month across 13 countries + You'll work in a remote-first, flexible environment with smart, humble people who care about quality and impact ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Enjoying SQL data pipelines with dbt](https://www.wearedevelopers.com/videos/823-enjoying-sql-data-pipelines-with-dbt) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [Modern Data Architectures need Software Engineering](https://www.wearedevelopers.com/videos/1030-modern-data-architectures-need-software-engineering) ## Related Articles - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)