Senior Data Engineer

The opportunity

We are looking for a Senior Data Engineer to own the core data infrastructure that powers collection, processing, and storage across the business. In this role, you will partner with data scientists, analysts, ML engineers, and other stakeholders to build dependable, scalable, and secure data systems that enable better decision-making company-wide. The position is centered on designing, improving, and supporting data platforms, pipelines, and governance practices that serve multiple data initiatives.

What you will do

• Design, build, deploy, test, and support large-scale data pipelines with a strong focus on analytics and machine learning use cases.
• Work with data scientists, ML engineers, and business stakeholders to make sure data remains accessible, consistent, and dependable for active projects.
• Create and maintain APIs that support data access and data manipulation, including integrations with outside data services when required.
• Oversee storage solutions for structured and unstructured data, including relational databases, text, images, audio, video, search engines such as Elasticsearch, and NoSQL systems.
• Understand data engines and data structures well enough to design solutions for transactional, analytical, and search workloads.
• Put in place data quality monitoring practices to ensure production data stays accurate and available for key users.
• Collaborate with ML engineers to troubleshoot data-related technical challenges and provide architecture guidance.
• Follow data security and privacy requirements and make sure solutions remain compliant.
• Keep documentation current, including data dictionaries, metadata records, and architecture diagrams.

What we are looking for

• A bachelor’s degree in Computer Science, Engineering, Mathematics, or a related discipline, or equivalent professional experience.
• At least 7 years of experience in data engineering.
• Strong programming ability in Python and SQL, with experience handling very large datasets at terabyte to petabyte scale.
• Practical experience with big data tools such as Spark using PySpark or Scala, and Flink.
• Exposure to machine learning frameworks such as TensorFlow, PyTorch, or similar tools.
• Solid knowledge of data warehousing or lakehouse concepts, ETL workflows, and data modeling.
• Experience building and integrating APIs for data services.
• Hands-on experience with cloud platforms, especially Azure.
• Working knowledge of DevOps practices, CI/CD pipelines, and container technologies such as Docker and Kubernetes.
• Experience with real-time or streaming data processing.
• Ability to work across technical teams and communicate clearly with multiple stakeholders.

Technical environment

The role uses Python and SQL, Trino as a query engine, Spark and Flink for big data processing, and a broad storage and database stack that includes ClickHouse, MySQL, PostgreSQL, MongoDB, Cassandra, HBase, and Redis. The environment also includes Azure, RESTful APIs, GraphQL, OpenAPI, Kafka, RabbitMQ, Docker, and Kubernetes.