Data Engineer

• Improve creator discovery across 400M+ profiles and billions of media files through retrieval, filtering, ranking, relevance, speed, and product decisions
• Build systems that generate and use embeddings from images, video, text, and audio at massive scale
• Evaluate models and technologies while balancing cost, latency, and quality
• Move promising search approaches from experiments into reliable production systems
• Shape problems, gather requirements, design architecture, write code, release features, measure outcomes, and iterate
• Inspect retrieval and ranking stages to identify and fix relevance issues
• Build and test multimodal embedding pipelines
• Collaborate with Data Insights on datapoint definitions, coverage, and data-quality requirements
• Test reranking models against real customer queries and assess relevance, latency, and inference cost
• Review production metrics, investigate regressions, and determine whether fixes belong in models, data, query logic, or product
• Collaborate with Data Core, Data Insights, product teams, customers, and company leadership
• Participate in intro, coding, system design, team, and CEO culture/alignment interviews

Senior Software Engineer – Data Search

• Independently develop and enhance software components in the areas of data engineering, analytics, and cloud
• Design, build, and optimize data pipelines, interfaces, and data-driven applications
• Contribute to architecture decisions, data models, and technical solution concepts
• Implement cloud-based data and software solutions on platforms such as AWS, Azure, GCP, or OCI
• Analyze complex technical requirements and translate them into robust, scalable, and maintainable solutions
• Work with relational and/or NoSQL databases
• Ensure quality through testing, code reviews, and technical documentation
• Collaborate closely with customers, project teams, and internal stakeholders
• Provide technical support and guidance to less experienced colleagues on the team

Senior Software Engineer, Data & Cloud

• Maintain and further develop data pipelines and data infrastructure
• Provide technical support for the CRM system and its underlying MySQL database
• Further develop the infrastructure using Linux, containerization, and isolated services
• Ensure data quality
• Optimize and automate existing data processes
• Independently implement projects and new ideas
• Contribute to Data Science and Machine Learning initiatives over time
• Collaborate with various departments
• Provide support with technical and data-related questions

Data Engineer – IT Support

• Conceptualize, implement, and further develop data models linking development, manufacturing, SAP, and supply-chain data
• Translate physical and process requirements into robust, traceable data models using OLAP/OLTP, Data Vault, and dimensional modeling
• Collaborate with process and domain experts to clarify definitions, thresholds, quality rules, and compliance requirements
• Design and implement data governance, quality checks, metadata management, and lineage tracking
• Implement production data pipelines using Kafka Streams, dbt transformations, Trino, Databricks, and CI/CD with quality gates and automated tests
• Ensure data consistency, visibility, and availability for analytics, AI/ML models, and simulations
• Develop performance and scaling strategies, including monitoring, profiling, and performance tuning
• Mentor less experienced Data Engineers, promote best practices, and conduct code reviews
• Contribute to architecture decisions, security-by-design, and data privacy requirements

Senior Data Engineer – f/m/d

• Develop, test, and operate robust ETL/ELT data pipelines
• Ingest, transform, and provide data from various sources
• Design and maintain data models, schemas, and structures for data warehouses and lakehouses
• Implement data validation, quality checks, and monitoring
• Build and optimize scalable cloud data solutions on AWS, Azure, or GCP
• Work with Infrastructure as Code, taking performance, scalability, and costs into account
• Automate recurring data flows, for example with Airflow
• Ensure clean, repeatable deployments through CI/CD
• Provide high-performance, well-documented datasets for data scientists and analysts
• Support the deployment of models and AI use cases to production
• Collaborate with cross-functional teams, data scientists, analysts, and clients
• Translate business requirements into robust technical solutions

• Design, build, and continuously enhance cloud-based Data & AI platforms
• Develop platform strategies and target architectures together with clients and implement them technically
• Design scalable and robust platform architectures as the foundation for data-driven solutions
• Automate the provisioning of modern cloud infrastructures on Azure or AWS using Infrastructure as Code
• Work with Terraform or OpenTofu and modern CI/CD processes
• Design and enhance Databricks platforms covering workspaces, compute, networking, identity, security, and governance
• Make architectural decisions and establish best practices for performance, security, governance, scalability, and cost efficiency
• Develop reusable platform blueprints, standards, and automations
• Evaluate new technologies and platform components and integrate them into existing architectures
• Support Data, Analytics, and AI teams with stable, high-performance platforms
• Contribute to technical concepts, architecture workshops, and the continuous improvement of standards
• Advise clients on implementing their Data & AI strategies

Data Platform Engineer

• Build and continuously enhance a domain-specific data foundation for the installment-loan business
• Develop scalable data pipelines and reliable data structures, for example using dbt
• Consolidate data from different sources and identify and manage dependencies
• Prepare data for reporting, analytics, and AI applications
• Develop reusable and reliable data products
• Structure and provide data for AI and automation use cases while adhering to data protection requirements, the GDPR, sensitive financial-data regulations, and secure data-access principles
• Collaborate with Product, Engineering, Data Analysts, and Data Scientists
• Translate business requirements into scalable data solutions
• Take end-to-end ownership of data products, from modeling and implementation through stable operation
• Independently prioritize and drive cross-team initiatives

Data Engineer – Fintech

• Develop and monitor robust, scalable data pipelines using Python, SQL, and Rundeck/Airflow
• Design performant data models and optimize complex SQL queries for high-scale environments
• Build automated checks and alerts to catch errors early and maintain accurate financial and operational reports
• Automate repetitive tasks and build self-service tools to improve data access across teams
• Experiment with and leverage AI-assisted coding tools to accelerate development and enhance code quality
• Translate business questions into practical, maintainable technical solutions with partners across the company
• Build and operate the data backbone serving as the organization’s single source of truth
• Support billing and company-wide reporting and analytics

Senior Data Engineer

• Teach and coach Data Engineering bootcamp participants
• Share professional knowledge and introduce complex topics to new practitioners
• Apply people leadership, project expertise, presentation, and communication skills
• Work with diverse teams and stakeholders
• Experiment with course material using agile methods to create strong student outcomes
• Deliver teaching online and on campus
• Take on assignments whose duration and frequency vary according to program needs

Freelancer Instructor – Data Engineering

• Take functional responsibility for the stable operation and ongoing development of the central data warehouse and ETL landscape
• Analyze data and process incidents
• Support sustainable resolution of issues
• Moderate ad hoc solution-finding sessions during operations
• Further develop internal expertise in data management and dashboard design, e.g. in Tableau
• Reduce reliance on external service providers
• Serve as the central point of contact for Finance and Risk Management
• Gather requirements, maintain the backlog, and prioritize implementation in a hybrid/agile working environment
• Collaborate with external service providers and internal stakeholders
• Continuously optimize the data platform

Data Engineer jobs in Germany