As a Sr.Data Quality Engineer manages and coordinates with internal or external parties the collection, compilation, normalization, and standard analysis of data assets across diverse projects and data platforms. Develops, maintains, evaluates, and tests valuates data solutions within an organization. Develops and executes plans, policies, and practices that control, protect, deliver, and enhance the value and integrity of the organization's data and information assets and programs.

Typical Functions

Manipulates and queries data by building SQL and stored procedures single handedly in Snowflake

Writes and executes test cases for data-related release items within Agile processes

Writes complex SQL queries and handle large data set quality validation

Builds a Data Quality Testing Framework from scratch to monitor the quality of data.

Understands the basic concepts of data Warehousing, ETL to validate changes to them

Executes common data quality tests to validate data accuracy, data completeness, data freshness, data integrity, data consistency

Builds test cases to validate the generated data analytical report and dashboard

Creates database tests to enforce data validation and constraints quality standards

Applies your knowledge of dimensional modeling and data warehouse concepts, such as star schemas, snowflakes, dimensions, facts to conduct data analysis

Performs statistical tests on large datasets to determine data quality and integrity

Evaluates system performance and design, as well as its effect on data quality

Collaborates with database developers to improve data collection and storage processes

Runs data queries to identify coding issues and data exceptions, as well as cleaning data

Gathers data from primary or secondary data sources to identify and interpret trends

Keeps abreast of developments and trends in data quality analysis

Collects, stores, processes, and analyses raw and/or complex data from multiple sources, recommends ways to apply the data, chooses and designs optimal data solutions, and builds data processing systems, using expertise in data warehousing solutions and working with the latest database technologies.

Maintains, implements, and monitors the quality of the data and information with the architecture used across the company; reports on results and identifies and recommends system application changes required to improve the quality of data in all applications.

Investigates data quality problems, conducts analysis to determine root causes of problems, corrects errors, and develops prototypes, process improvement plans across all programs, and proof of concepts for the selected solutions.

Processes unstructured data into a form suitable for analysis, followed by doing the analysis.

Integrates innovations and algorithms into the organization's data systems, working closely with engineering team.

Implements complex data projects with a focus on collecting, parsing, managing, analysing, and visualising large sets of data to turn information into insights using multiple platforms.

Serves as a data subject matter expert, collaborates with business owners or external clients to establish an analysis plan to answer key business questions, and delivers both reporting results and insights.

Generates specific and operational reports in support of objectives and initiatives, and presents and communicates complex analytical data and results to appropriate audiences.

Requirements

Other duties or functions may be assigned.

Bachelor's Degree in Engineering, Computer Science, or equivalent experience

6+ Years of Software Quality Assurance experience which includes Data Warehouse Testing

Proficiency in SQL ideally within Snowflake

SQL with MySQL, SQL Server, and/or PostgreSQL

Experience with Sisense or Power BI

5+ years experience developing data driven software

Proficiency in programming languages, including Structured Query Language (SQL)

Experience writing DBT tests

Experience In Agile Methods, Particularly Scrum, Preferred

Demonstrated knowledge of critical thinking

Professional experience around the data science lifecycle (feature engineering, training, model deployment)