Senior Data Engineer, Platform Data

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🕒 May 26

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Leadfeeder

WebsiteLinkedInAll Job Openings

51 - 200 employees

💰 Series A on 2019-04

Leadfeeder is a website visitor analytics software that shows you the companies visiting your website, how they got there, and what pages they clicked. Think of us like your lead generation, account-based marketing, and sales intelligence tool all rolled into one, like a burrito. Simply install the Leadfeeder Tracker script in less than 5 minutes! The Leadfeeder Tracker will reveal up to 30% more companies in close to real-time (fresh leads every hour). You can try Leadfeeder Premium free for 14 days (no credit card required). Plus, we integrate with multiple tools: Google AnalyticsMailChimp PipedriveSalesforceHubSpotAnd, many more! We are an international startup headquartered in Helsinki, Finland.

📋 Description

• 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

• 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

• 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.