Senior Data Engineer (pharmaceutical solutions)

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Data Engineering





Opinov8 is a technology service provider, with an Opinion in Innovation, we engage with our clients at any stage in their product engineering and innovation goals, with solutions being mainly collaborative and outcome-driven. We are driven by delivering value, for our clients, through technological innovation.
We are a young energetic and dynamically agile company and the team behind Opinov8 are software industry veterans. We are ambitious on our own and on our client’s behalf and we do not look at a client engagement as a project, we are in it to help our clients build sustainable products, platforms, and businesses.
Our core values and what we look for in our Opinov8rs:
You are Collaborative - which is a key component to being successful in distributed environments.
You are Innovative - it is a requirement that we innovate, as individuals and as a company.
Be Adventurous - courageous people are adventurous, we are hiring and nurturing this quality.
Always Respectful - remember if you don’t respect others they will not respect you.
Inherently Intelligent - it goes without saying we are all intelligent in our own way.
Ultimately Responsible - being responsible is a core personality trait that is needed for us and our clients’ success and ultimately a happy work environment.
Senior Data engineer with extensive hands-on experience in designing and implementing data pipelines using Apache Spark. Ideally the profile knows the complete data stack from both the engineering and analytics sides. Besides being responsible for building ETL data pipelines — the senior Data engineer is expected to assume following responsibilities:   
- Model reusable datasets;
- Establish and maintain the data documentation to ensure that business and data analysts use the same definitions and language. This involves providing identifiable and understandable descriptions of data as well as exposing them in a way for all consumers to easily find answers to their queries; 
- Define data quality rules, standards, and metrics; 
- Set software engineering best practices for analytics (DataOps);
- Close collaboration with other team members and business stakeholders.
Solid knowledge of:
- Git   
- Sql   
- Databricks   
- Delta lake   
- Docker   
- Public cloud Azure   
- Spark   
- Python
Experience with:
- Confluent Kafka   
- NoSql db (keyvalue, document, etc.)
- Highly scalable db solutions (any cassandra, clickhouse, etc.)
- Orchestrations tools (Airflow or similar)
Good additional skills and experience   
- Terraform   
- Data quality tools (GreatExpectations or similar)   
- .NET Core with Dapr   
- React.js (in minimal scale for occassional support of admin client)


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