Senior Data Engineer

technologies-expected :
PythonSQLPostgreSQLAWSGoogle Cloud PlatformMicrosoft Azuretechnologies-optional :
AirflowPrefectDagsterSparkKafkaBigQueryRedshiftSnowflake Data CloudHadoopKinesisFlinkabout-project :
We are seeking a highly motivated and experienced Senior Data Engineer to design, build, and manage the data infrastructure that fuels our AI-driven diagnostic platform.
This is a critical role where you will lay the foundation for how we collect, store, process, and utilize complex medical data, enabling breakthroughs in rare disease diagnosis.You will be responsible for creating robust, scalable, and reliable data pipelines and systems.
While navigating the requirements of our on-premise infrastructure for sensitive data, you will also leverage cloud environments for specific workloads and contribute to our evolving data strategy, potentially including future hybrid cloud architectures.
Your expertise will be crucial in ensuring our Data Scientists and Medical teams have access to high-quality, timely data.Your Impact:
As a Senior Data Engineer, you are central to unlocking the potential of our data and directly contributing to our mission:
Enable AI Discovery:
Build the data pipelines and structures that provide the high-quality fuel for our life-changing AI models.Ensure Data Integrity & Trust:
Implement systems and processes to guarantee data quality, security, and compliance within a regulated healthcare environment.Shape Our Data Foundation:
Play a key role in designing our data architecture, selecting tools, and defining best practices for data management and governance as we grow.Drive Efficiency & Scalability:
Optimize data processing and storage solutions to handle increasing data volumes and complexity efficiently.Foster Innovation:
Explore and implement cutting-edge data technologies and techniques appropriate for our unique challenges.responsibilities :
Data Pipeline Development:
Design, build, automate, and maintain scalable and reliable ETL/ELT pipelines for diverse data sources.Data Modeling & Architecture:
Design and implement effective data models and database schemas; contribute to the overall data architecture design.Data Storage Management:
Manage and optimize our data storage solutions, including our current PostgreSQL databases and potentially other SQL/NoSQL/Data Lake solutions in on-premise and cloud environments.Data Quality & Governance:
Implement data quality checks, monitoring, and anomaly detection; contribute to data governance policies and ensure compliance (e.g., GDPR).Performance Optimization:
Monitor and optimize the performance of data pipelines, queries, and data retrieval processes.Tooling & Technology:
Evaluate, select, and implement appropriate data engineering tools and technologies.Collaboration:
Work closely with Data Scientists, MLOps/DevOps Engineers, and domain experts to understand data requirements and deliver solutions.Security:
Ensure data security best practices are implemented throughout the data lifecycle.Documentation:
Maintain clear documentation for data pipelines, schemas, and processes.Mentoring:
Support the development and mentor less experienced team members.requirements-expected :
Experienced Data Engineer:
Proven track record in a Data Engineering role, with experience operating at a senior level.Strong Programmer:
Proficiency in Python is required for data manipulation, pipeline development, and scripting.SQL & Database Expert:
Deep understanding of SQL and extensive experience with relational database systems (including current hands-on experience with PostgreSQL).
Experience with database design and data modeling is essential.Pipeline Builder:
Hands-on experience designing, building, and operationalizing complex data pipelines using relevant tools (e.g., Airflow, Prefect, Dagster, Spark, Kafka, or cloud-native equivalents).Data Warehousing Knowledge:
Solid understanding of data warehousing concepts, dimensional modeling, and associated technologies (experience with cloud DWs like BigQuery, Redshift, Snowflake is a plus).Cloud Data Savvy:
Experience with data services in at least one major cloud provider (AWS, GCP, Azure).Data Quality Advocate:
Understanding of data quality principles and experience implementing relevant checks and monitoring.Infrastructure Aware:
Familiarity with Linux environments and comfortable working with infrastructure components relevant to data engineering.Proactive & Self-Motivated:
Thrives in a fast-paced startup environment, takes initiative, identifies problems, and drives solutions independently.Collaborative Communicator:
Excellent communication skills to work effectively across technical and non-technical teams.Passionate:
Genuine interest in applying data engineering skills to solve real-world problems in healthcare.offered :
A chance to make a significant impact on patient lives through data-driven insights.A pivotal role in building the data foundation of a growing HealthTech startup.Opportunity for professional growth in a challenging and rewarding field.A dynamic, innovative, and collaborative startup culture that values initiative.Competitive salary.benefits :
sharing the costs of sports activitiesprivate medical caresharing the costs of foreign language classessharing the costs of professional training & coursesflexible working timeintegration eventscorporate library