Remarque Systems is looking for a Principal Data Developer to join our Clinical Technologies team.
Remarque Systems is a clinical technology company that is passionate about improving drug development using cutting edge software. Remarque is designed to simplify study management and propel innovation by consolidating data and support strategic decision making.
What you will be doing:
- Design, develop and implement resilient and performant data processing platforms, leveraging latest technologies and methodologies, including Big Data
- Design, develop and run a data infrastructure that scales
- Collaborate with stakeholders to unlock value found in data and make it visible to others
- Establish and standardize data practices, and lead and mentor others in them
- Own data security standards
- Continually monitor and optimize data stores and integrations
What we are searching for:
- BS/MS in Computer Science, Engineering, or related field, or equivalent experience
- BS degree and 10-15 years’; MS degree and 6 years’; or an equivalent experience of 15+ years with direct experience designing and building scalable data platforms
- Analysis of large initiatives with a proven success record of implementation of these initiatives
- Large-scale data warehousing, reporting, and analytics development experience
- Strong Modeling experience using any modeling tools
- Strong ETL experience using any one of the ETL tools (Talend, WSO2, Data Stage, SSIS etc.)
- Experience in at least one of the leading BI tools like Tableau, Microsoft Power BI, SAS Enterprise, QlikSense etc.
- Significant exposure to distributed data processing platforms with Big Data technologies like Hadoop, Hive, Pig, Oozie, MapReduce, Spark, Shark, Storm, RabbitMQ, Flume, etc.
- Experience with one or more relational and NOSQL data stores and distributed in-memory solutions such as MySQL, PostgreSQL, Cassandra, etc.
- Drive and participate in hands-on development for rapid prototyping to facilitate discussion and ideation with business partners
- Experience in data lifecycle management including data ingestion, data quality validation, audit support, archive and purge processes
