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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer, Platform Data - **Company:** Leadfeeder - **Location:** Germany (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, BigQuery, Cloud Computing, Code Review, Continuous Integration, Data Infrastructure, Data Transformation, Data Systems, Data Warehousing, Distributed Data Store, Identity and Access Management, Python (Programming Language), Operational Databases, Performance Tuning, Standard Sql, Software Engineering, Usage Analysis, Datadog, Cloud Platform System, Snowflake, Low Latency, Apache Flink, Real Time Data, Apache Kafka, Spark Streaming, Machine Learning Operations, Terraform, Stream Processing, Amazon Redshift, Databricks - **Published:** May 28, 2026 - **Apply:** https://de.indeed.com/viewjob?jk=048759755c8f847e ## About the Role Do you have experience in Terraform?, * 10+ years of hands-on experience in data and/or software engineering, with a leading role in production data pipelines that power product or customer-facing systems (not only internal analytics). * Strong engineering background - production-grade Python, strong SQL, code review, testing, CI/CD, and operational ownership are second nature. * Deep cloud infrastructure experience - AWS (S3, Kinesis/MSK, Lambda, ECS/EKS, IAM, networking) or equivalent; comfortable with infrastructure-as-code (Terraform, CDK, or similar). * Experience with streaming or real-time data ingestion (Kafka, Kinesis, Flink, Spark Streaming, or similar) into a warehouse or lakehouse environment. * Solid experience with modern data warehouse / lakehouse technologies (Snowflake, BigQuery, Redshift, Databricks, Athena or similar). * Hands-on experience with data transformation tooling, particularly dbt. * Track record of building and operating distributed data systems at scale - with deliberate attention to performance, reliability, and cost. * Familiarity with data quality and observability tooling and practices (Great Expectations, dbt tests, Monte Carlo, or similar). * Background in enabling AI/ML workloads on top of production data. * Strong communication skills in English, both written and verbal, with the ability to collaborate effectively with engineering, product, and non-engineering stakeholders. * Comfortable working in a fully remote environment. * Be physically located within Europe. Nice to have * Knowledge of data cataloguing tools, data contracts frameworks, or data mesh principles. * Background in B2B SaaS and familiarity with intent data, web event data, CRM, product analytics, billing, and support tooling. ## Description We are looking for a Senior Data Engineer to join our Platform Data team and take a leading role in our production data pipelines - the systems that power Leadfeeder's product experience, from ingesting and enriching web visit signals to delivering intent data and account intelligence to customers in near real time. This is a deeply technical, engineering-led role focused on the production data layer - the pipelines, streaming infrastructure, and cloud systems that turn raw signal into product. Reliability, scale, and engineering quality are the bar. The platform you build is what customers experience every day. You will work closely with product and engineering teams to design, build, and operate the pipelines, streaming systems, and cloud infrastructure that move data through Leadfeeder at scale. You will shape how we ingest, process, enrich, and serve production data - defining the standards, tooling, and architecture that make our data platform a competitive advantage., * Design, build, and operate production data pipelines that power Leadfeeder's product features - from ingestion through enrichment, processing, and serving. * Build and maintain streaming and real-time ingestion systems that move event data through the platform at scale and with low latency. * Own the cloud infrastructure underpinning the pipelines - compute, storage, networking, security, observability - designed and managed as code. * Collaborate with product and ML engineers to deliver datasets and pipelines that power product-facing features and AI/ML workflows. * Implement data quality, observability, and reliability controls across the pipelines so issues are caught early, incidents are short, and downstream teams can trust the data. * Drive engineering practices across the team: code review, testing, CI/CD for data, infrastructure-as-code, performance tuning, and cost discipline. * Partner with engineering, product, and ML teams to translate product requirements into scalable, well-documented data systems. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [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) - [The OpenTelemetry mistakes I keep seeing (and how to stop making them)](https://www.wearedevelopers.com/videos/100158-the-opentelemetry-mistakes-i-keep-seeing-and-how-to-stop-making-them) - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [Data Governance in the Era of AI](https://www.wearedevelopers.com/videos/1622-data-governance-in-the-era-of-ai) - [Making Data Warehouses fast. 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