Position: Automation QA (Python)
Company: DICEUS
Category: QA Automation
Experience required: 3y
English level: intermediate

The primary goal of this role is to ensure that high-quality product is delivered to stakeholders and customers.

Responsibilities

Work in conjunction with Data Engineers to ensure high-quality Data Deliverable, help identify inconsistent data patterns, and how they are manifested from the source data
Design, implement, and use data quality frameworks to support the process of identifying inconsistent data patterns
Write DB scripts to validate data in the data repositories against the data in the source systems
Track, monitor, and document testing results
Analyze complex data systems to develop automated and reusable solutions for extracting requested information while assuring data validity and integrity
Perform tasks spanning the full lifecycle of data management activities with minimal supervision

Requirements
2+ years of experience working with Databricks, Airflow, Hive, Hadoop, Jupyter Notebooks
4+ years of professional data engineering working experience.
4+ years of experience with database software (Postgres, MySQL, etc), in writing complex SQL queries and stored procedures, and ETL in Python.
Experience with US Healthcare data is a plus.
Experience working in virtualized cloud environments including cloud-based IaaS/SaaS/PaaS solutions.
Understanding of ETL methodologies and Data Warehousing principles, approaches, technologies, and architectures including the concepts, designs, and usage of data warehouses and data marts.
Knowledge of data warehousing, OLAP, multi-dimensional, star, and snowflake schemas
Knowledge and experience with database design principles including referential integrity, normalization, and indexing to support application development
Strong understanding and experience in development activities for all aspects of the Software Development Life Cycle (SDLC)
Bachelor’s degree or master’s degree in a quantitative field such as Computer Science and Information Systems, Database Management, Big Data, Data Engineering, Data Science, Applied Math, etc.