Data Engineer (Modern Data Platform & AI)
Role details
Job location
Tech stack
Job description
With a mission to empower 15.6 million house owners across Germany and the broader DACH region, we at Aroundhome are building a platform where every strategic decision is powered by data. We're a team of around 250 people, and to accelerate our journey, we're looking for an experienced Senior Data Engineer (Modern Data Platform & AI) to contribute in shaping our data foundation together with our Data Engineers.
You will report to the Data Platform Team Lead and work closely with different stakeholders to ensure a reliable, scalable, and accessible data platform for the whole company. Touchpoints with other functional areas include Product, Marketing, Finance and Product Analytics.
- Data Modeling & Transformation:
- Design necessary data models and transformations to curate raw data.
- Develop, optimize and maintain existing data models, pipelines, and transformations to support analytics, reporting, and AI use cases such as but not limited to curating, transforming, annotating and modeling data.
- Data Platform Architecture:
- Architect and contribute in implementing a scalable, modern data platform, including data lakehouse or warehouse, to support real-time/near-real-time data flows from Kafka to downstream consumers.
- Optimize ETL/ELT pipelines using tools like DBT, Spark, or Airflow, bridging upstream (e.g. Debezium, MSK) and downstream processes.
- Evaluate and integrate new technologies to support hybrid monolith-microservices architecture and ML and AI enablement.
- Ensure seamless migrations and minimal disruptions during platform evolution.
- Real-Time Data Integration: Build and optimize real-time data pipelines using Kafka, Spark, and Delta Live Tables.
- Data Governance & Quality:
- Support the team lead in establishing and enforcing data governance frameworks, including data lineage, quality standards, catalogue, metadata management, SSOT for business glossaries/CBC terms, and policies to ensure reliable reporting.
- Ensure the existence of, or adaptation to, full Data Life Cycle Management (DLCM) and end-to-end testing.
- AI/ML Enablement: Collaborate with the team to integrate AI/ML capabilities, such as feature engineering and model serving, to accelerate data products for market penetration and operational efficiency, as well as operationalizing ML models and integrate AI into business processes.
- Knowledge Sharing: Mentor the team on best practices, modern tools (e.g., Databricks, Snowflake, AI adaptation and integrations like Cursor/CodeRabbit), and cloud-native scalability. And last but not least foster a culture of innovation and continuous improvement.
- Stakeholder Collaboration: Collaborate with Product Analytics, domain teams, and business to deliver data solutions that drive value and are aligned with business needs.
Requirements
- Master's degree in Computer Science, Data Engineering, or related field (or equivalent experience)
- 10+ years of experience in data engineering, with 5+ years in senior roles focused on modern architectures.
- Excellent communication and collaboration skills, the ability to drive change and influence stakeholders, and a passion for mentoring, coaching, and sharing knowledge
- Proven expertise in designing, developing & maintaining data lakehouses/DWH (e.g., Databricks Delta Lake, Snowflake) and transformations (e.g., DBT, SQL/Python/Spark).
- Strong experience with cloud platforms such as AWS services (S3, Athena, MSK/Kafka, Terraform) and real-time streaming (e.g., Kafka, Spark Structured Streaming, Flink).
- Hands-on knowledge of data governance tools (e.g., Unity Catalog, Collibra) for lineage, quality, catalogs, and SSOT.
- Familiarity in AI/ML pipelines and MLOps (e.g., MLflow, feature stores) and complex system integration within modern data technologies.
- Proficiency in CI/CD for data, and tools like Git, Airflow, or dbt Cloud.
- Experience with large-scale data modeling (DataVault, dimensional, schema-on-read) and optimizing for self-service analytics.