DirecciónOur Purpose
P S L Group is a global organisation dedicated to putting information at the service of medicine. The companies and people of the P S L Group aim to improve medical care by serving those who need it, those who provide it and those who seek to improve it.
Our primary purpose is to help clients increase the effectiveness of activities pertaining to scientific communication, medical education and product/service marketing. To this end, we want our information services to contribute to the goals we share with our clients, namely: to accelerate the advancement of medicine and help people enjoy better, longer lives.
Position Summary
If you are a Data Engineer with a craving for making sense out of structured and unstructured data with the goal of affecting people’s lives in a positive manner, please read on!
We are looking for a Data Engineer that will work on the collecting, storing, processing, and analyzing of huge sets of data. The focus will be on working with the Data Engineering Team to design technologies that will wrangle, standardize and enhance our master data and transactional data repositories, then build operational and monitoring processes to govern that data. You will also be responsible for federation of this data across the enterprise using batch, streaming and microservices architectures.
Unique skills expected for this job are the ability to write clean, high-quality Python libraries that can be re-used within our platform; ability to create orchestration workflows that ingest structured and unstructured data in both streaming and batch modes; enrich and make it available for use throughout the enterprise.
What you will do
- Build the infrastructure required for optimal ETL of data from a wide variety of data sources using Python, AWS services and big data tech
- Create and maintain enterprise-wide integration pipelines leveraging Kinesis, Glue, Step Functions, Lambda, and general microservices microbatch architecture best practices
- Manage databases running on PostgreSQL, Snowflake, Redis, Redshift, ElasticSearch, Redis, Ne04j
- Monitor performance using Cloudwatch, Cloudtrail and advise on necessary infrastructure changes as needed
- Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, redesigning infrastructure for greater scalability, etc.
- Work with stakeholders including the Executive, DataOps and Business teams to assist with data-related technical issues and support related data infrastructure needs.
- Create data tools for analytics and data scientist team members that assist them in building and optimizing our enterprise data hub into an innovative industry leader.
Who you are
- Understanding of database design (both SQL and noSQL)
- Good working SQL knowledge and experience working with relational databases - both operational DBs and data warehouses
- Experience working with unstructured datasets
- Experience with relational SQL and NoSQL databases, including PostgreSQL, MSSQL, Redis, MongoDB
- Excellent analytical and problem solving skills
- Experience with data cleansing, data wrangling, data quality, standardization, transformations etc
- Experience with ETL data pipeline tools: Luigi, Airflow, Streamsets, Informatica etc.
- Experience with object-oriented/scripting languages: Java, Python etc
- Preferred: Application Integration experience leveraging microservices and microbatching
- Preferred: 1+ years experience implementing solutions in the cloud (AWS, GCP, Azure) leveraging AWS services like S3, API Gateway, IAM, Lambda, SQS
- Preferred: Experience with build systems: github, bitbucket, jenkins, jira, terraform
- Preferred: Prior experience with Master Data Management
- Preferred: BS/MS in Math, Computer Science or equivalent experience
