Sr. Data Engineer

Take on a new challenge and apply your data engineering expertise in a cutting-edge field. Youll work alongside collaborative and innovative teammates.

You'll play a key role in enabling data-driven decision-making across the organization by ensuring data availability, quality, and accessibility. Day-to-day, youll work closely with teams across the business (e.g., Data Scientists, Analysts, and ML Engineers), mentor junior engineers, and contribute to the architecture and design of our data platforms and solutions.

Youll specifically take care of designing and developing scalable data pipelines, but also managing and optimizing object storage systems.

Well look to you for:

• Designing, developing, and maintaining scalable and efficient data pipelines using tools like Apache NiFi and Apache Airflow.
• Creating robust Python scripts for data ingestion, transformation, and validation.
• Managing and optimizing object storage systems such as Amazon S3, Azure Blob, or Google Cloud Storage.
• Collaborating with Data Scientists and Analysts to understand data requirements and deliver production-ready datasets.
• Implementing data quality checks, monitoring, and alerting mechanisms.
• Ensuring data security, governance, and compliance with industry standards.
• Mentoring junior engineers and promoting best practices in data engineering.

All about you

We value passion and attitude over experience. Thats why we dont expect you to have every single skill. Instead, weve listed some that we think will help you succeed and grow in this role:

• Bachelors or Masters degree in Computer Science, Engineering, or a related field.
• 7+ years of experience in data engineering or a similar role.
• Strong proficiency in Python and data processing libraries (e.g., Pandas, PySpark).
• Hands-on experience with Apache NiFi for data flow automation.
• Deep understanding of object storage systems and cloud data architectures.
• Proficiency in SQL and experience with both relational and NoSQL databases.
• Familiarity with cloud platforms (AWS, Azure, or GCP).
• Exposure to the Data Science ecosystem, including tools like Jupyter, scikit-learn, TensorFlow, or MLflow.
• Experience working in cross-functional teams with Data Scientists and ML Engineers.
• Cloud certifications or relevant technical certifications are a plus.