Data Engineer

lemlist

. Design, build, and improve a scalable data platform supporting product data solutions .

Posted 9/15/2026full-timeRemote • FranceMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and managing scalable data platforms, with a strong focus on data quality, governance, and integration. Proficient in cloud-based technologies and data engineering best practices to support product analytics and operational efficiency.

Highest-signal resume keywords

Big Data ArchitectureSQL ProgrammingData Pipeline DevelopmentData GovernanceCI/CD Practices

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data EngineeringData ModellingData IntegrationData Quality AssurancePython ProgrammingSpark ProgrammingSQLBigQuerySnowflakeDatabricks

Soft Skills

Analytical MindsetProblem SolvingHigh OwnershipExecution Speed

Tools & Technologies

Pub/SubKafkaDockerGitJenkins

Certifications & Qualifications

Master's Degree in Computer Science

Industry Keywords

Data PlatformsCloud InfrastructuresData WarehousingData LakesDataOpsB2B SaaS

Tech Stack

Tools & technologies

BigQueryCloudDistributed SystemsDockerJenkinsKafkaNoSQLPythonSparkSQL

About the role

Key responsibilities & impact

Design, build, and improve a scalable data platform supporting product data solutions
Collaborate with product and business teams to build scalable and agile solutions
Define technical standards and contribute to data platform architecture and deployment decisions based on the strategic product roadmap
Develop, deploy, and manage efficient data platforms and automated data pipelines using cloud-based and on-premise technologies
Design, maintain, and enhance key data product features for quality, certification, accessibility, and integration
Analyze and develop data operations and pipelines following enterprise guidelines and best practices
Apply data quality processes, governance, and catalog/glossary curation
Maintain and improve existing pipelines by integrating new features and change requests using an agile approach
Ensure data quality, lineage, versioning, and observability across the stack
Support CI/CD and release processes
Onboard the existing data platform end to end, including sources, ingestion jobs, warehouse models, orchestration, BI layer, and consumers
Audit the current stack, identify risks and gaps, and estimate their costs
Deliver a quick win such as fixing an unreliable pipeline, resolving a cost anomaly, or making a critical dataset trustworthy
Create and validate a technical roadmap covering target architecture, technology choices, and migration path
Improve data engineering standards for repository structure, Git workflow, CI/CD, environments, code review, and deployment
Scale the data platform with data volume and product growth while controlling pipeline costs
Reduce incidents and time-to-detect for critical datasets
Implement observability with freshness, volume, and schema checks, alerting, SLAs, and ownership
Enable new product analytics, in-product data, and ML/AI use cases
Serve as a data architecture reference for the C-suite and Product

Requirements

What you’ll need

Master's degree in computer science, distributed systems, data engineering, engineering or equivalent
5+ years experience in intensive data platforms in the context of Big Data and cloud infrastructures/platforms
Strong background in Big Data architecture approaches and DBMS/Data Warehouse modelling, optimisation, and management
Deep knowledge of SQL, Python and Spark-related programming languages
Experience with data warehouses and lakes, including BigQuery, Snowflake, Databricks, Storage, and Delta Lake
Extensive expertise in data preparation, integration, modelling, and governance processes
Proven experience designing and managing end-to-end production-ready solutions
Experience developing, optimising, and maintaining scalable data ingestion and transformation pipelines using modern data technologies, including Pub/Sub and Kafka
Familiarity with DataOps: Git, Docker, CI/CD practices including Jenkins, and deployment workflows in a data engineering environment
Experience ensuring data quality, consistency, and performance across data platforms while applying data governance principles
Strong analytical mindset and ability to solve complex data challenges and continuously improve data solutions
Ability to thrive in a demanding, fast-growing environment with high ownership, structure, and execution speed
Fluent in French and English
Nice to have: hands-on experience with NoSQL, Search DBMS, or OLAP DBMS
Nice to have: first experience in B2B SaaS

Benefits

Comp & perks

Competitive salary and company bonus (up to 18K€ per year depending on company’s performance)
38 days of holidays/year
Alan Blue: Comprehensive 100% premium medical coverage for you and your family
Swile Meal Tickets: Enjoy daily meal tickets to fuel productivity
Navigo Card: Seamless commuting with a 100% covered Navigo card
Laptop, tools, and equipment needed for the job
Team building: annual company gathering at locations around the world