We are seeking an experienced Senior Data Engineer to contribute to designing and building scalable SaaS products within our AI Lab. In this role, you’ll combine deep technical expertise with strategic vision to build AI-powered products that will help transform our clients’ business models and enable their growth.
Simon-Kucher is at the forefront of innovation in driving commercial excellence, revamping business models, developing solutions and methodologies for unlocking better growth of our clients. Within AI Lab, we are developing cutting-edge large scale AI products to deliver sustained top-line impact for our clients.
Are you interested in working in a team of AI evangelists with a can-do attitude? Want to experience the dynamics of agile processes in open-minded teams? How about getting creative in a startup atmosphere with a steep development curve and flat hierarchies? And most importantly, do you want to make a difference? Then youve come to the right place.
responsibilities :
Develop and maintain data architecture: create and manage robust data architectures that support high-volume, high-throughput SaaS applications, focusing on reliability and scalability.
Design and implement batch/stream pipelines (CDC, API, files) with schema evolution, idempotency, and data quality gates.
Integrate internal and external data sources, structured and unstructured (e.g., pricing databases, market benchmarks, CRM).
Model core entities and features; choose storage layouts and partitioning; build reusable data products.
Implement entity resolution and fuzzy matching; evaluate and tune matching quality.
Implement ETL/ELT processes: develop processes for extracting, transforming, and loading data from multiple sources into data warehouses or lakes for analytical use.
Ensure data quality and security: implement data validation, cleansing routines, and security measures, including encryption and access controls, to ensure data accuracy, privacy, and compliance with regulations.
Own orchestration, lineage, and observability; define SLAs and error budgets.
Partner with Product Owner to translate customer needs into scalable data and ML solutions.
Partner with ML/MLOps on feature pipelines.
Work with Cloud Platform Engineer to deploy and manage services securely.
requirements-expected :
6+ years in data engineering at scale; strong Python/SQL; Spark or Flink; Parquet/columnar formats.
Experience with big data processing frameworks like Apache Spark and messaging systems like Kafka.
