Senior Data Engineer, AI & Data Platform

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🕒 May 6

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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 maintain the internal data warehouse and analytical data layer, consolidating data from across our product and operational systems into a single reliable source of truth. • Define and enforce data models, schemas, and data contracts so that downstream consumers — data analysts and business teams — can trust and self-serve the data they work with. • Build and maintain transformation pipelines that turn raw internal data into clean, structured analytical datasets ready for BI, reporting, and AI use. • Collaborate with Data Analysts to enable AI and machine learning use cases on top of internal data — building the datasets and infrastructure they need to train models and run analytical workflows. • Implement data quality monitoring, lineage tracking, and observability across the warehouse so issues are caught early and data reliability is maintained over time. • Work with stakeholders across engineering, product, and business teams to understand their data needs and translate them into scalable, well-documented data models. • Champion good data engineering practices across the team: CI/CD for data assets, testing, documentation, and reproducibility.

🎯 Requirements

• 10+ years of hands-on experience in data engineering, with demonstrated ownership of production data warehouses or analytical data platforms. • Strong proficiency in SQL and Python. • Solid experience with modern data warehouse technologies (Snowflake, BigQuery, Redshift, or similar). • Experience with AWS data services (S3, Athena, Glue, or equivalents). • Hands-on experience with data transformation and modelling tools, particularly dbt. • Experience with workflow orchestration tools such as Apache Airflow or similar. • Background in enabling AI workloads on top of warehouse data. • Solid understanding of dimensional modelling, data vault, or other analytical data modelling approaches. • Familiarity with data quality tooling and testing practices (Great Expectations, dbt tests, or similar). • Strong communication skills in English, both written and verbal, with the ability to collaborate effectively with non-engineering stakeholders. • Comfortable working in a fully remote environment. • Be physically located within Europe.

🏖️ 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.