Senior Data Engineer, Platform Data

Leadfeeder

Senior Data Engineer designing and operating production data pipelines for a B2B lead generation platform. Collaborating across teams to ensure data integrity and scalability.

Posted 5/26/2026full-timeRemote • 🇩🇪 GermanySeniorWebsite

Tech Stack

Tools & technologies

Amazon RedshiftAWSBigQueryCloudKafkaPythonSparkSQLTerraform

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

The chance to work with a very knowledgeable, high-achieving and fun team.
An international, diverse, dynamic and committed work environment.
The opportunity to work remotely, with a flexible work schedule.
Mental health support with Auntie.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

PythonSQLCI/CDinfrastructure-as-codestreaming data ingestiondata transformationdata qualityobservabilitydistributed data systemsAI/ML workloads

Soft Skills

strong communication skillscollaborationoperational ownershipcode reviewtestingperformance tuningcost discipline