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Extracting actionable insights from broad, open-ended questions to influence investment decisions
Developing common data analysis workflows using NLP (sentiment analysis), Geospatial analysis and customer segmentation methods
Designing a common library for other data scientists that includes storage connectors, proxy authentication, secrets management, standard data visualization methods, and more
Designing and implementing ML/DL models for text and customer segmentation data
Designing and building AWS infrastructure together with the Data Engineering team
Developing data technology stack including API services and ETL pipelines
Designing datasets and schemes for consistency and easy access
Extracting actionable insights from broad, open-ended questions to influence investment decisions
Developing common data analysis workflows using NLP (sentiment analysis), Geospatial analysis and customer segmentation methods
Designing a common library for other data scientists that includes storage connectors, proxy authentication, secrets management, standard data visualization methods, and more
Designing and implementing ML/DL models for text and customer segmentation data
Designing and building AWS infrastructure together with the Data Engineering team
Developing data technology stack including API services and ETL pipelines
Designing datasets and schemes for consistency and easy access
Cooperation with the international, 5-6 people team - 2x Software Engineers, 1x Senior Data Scientist, 1x Data Scientist, 2x Junior Data Engineers
Work is organized in 2 weeks of Agile sprints and daily stand-up is at 16:30 PL timezone
Stack of the projects included tools and technologies like Python, SQL, PySpark, Pandas, Azure Cloud, GCP Cloud, AirFlow (with DAGs), Snowflake, CI/CD, Docker, Kubernetes, Terraform
Apply data engineering practices and standards to develop robust and maintainable solutions
Actively involved in every part of the software development life cycle
Experienced at guiding non-technical teams and consultants in best practices for large-scale data engineering
Enjoy collaborating in teams to share software design and solution idea
Answering issues and pull requests in GitHub, questions on Slack
Reaching out to existing/potential users to assist in adoption
Implementing new features, fixing bugs, suggesting improvements
Writing more tests, anticipating more edge cases
Improving the CI flow on GitHub, to support testing for more databases
Assisting in the development of new modules (e.g. same-db data-diff)
Improving documentation, and writing tutorials
Verify that all data was copied when doing critical data migration. For example, migrating from Heroku PostgreSQL to Amazon RDS
Verifying data pipelines. Moving data from a relational database to a warehouse/data lake with Fivetran, Airbyte, Debezium, or some other pipeline
Alerting and maintaining data integrity SLOs. You can create and monitor your SLO of e.g. 99.999% data integrity, and alert your team when data is missing
Debugging complex data pipelines. When data gets lost in pipelines that may span a half-dozen systems, without verifying each intermediate datastore it’s extremely difficult to track down where a row got lost
Detecting hard deletes for a updated_at-based pipeline. If you’re copying data to your warehouse based on a updated_at-style column, then you’ll miss hard-deletes that data-diff can find for you
Make your replication self-healing. You can use data-diff to self-heal by using the diff output to write/update rows in the target database
Right now we are looking for a friendly, bright, extremely well-organized and hardworking individual to fill the role of an Office Assistant at our location in Warsaw
You will be responsible for:
- Contact with suppliers, couriers and distribution of orders in the office
- Management of office equipment and hardware for employees
- Taking care of guests and clients visiting the company