Create and maintain optimal data pipeline architecture,
Assemble large, complex data sets that meet functional / non-functional business requirements.
Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and various cloud technologies.
Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
Work with the business to assist with data-related technical issues and support their data infrastructure needs.
Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.
Work with data and analytics experts to strive for greater functionality in our data systems.
Create complex SQL queries and database objects (stored procs, views, etc.) to pull and manage data.
Developing complex data pipelines using SSIS packages, Azure Data Factory, or other related ETL/ELT tools to move and translate data
Create and maintain documentation on data pipelines
Requirements And Qualifications
Bachelor’s degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field; or equivalent combination of education experience and training that provides the required knowledge and skills.
4 – 6 years of experience in a Data Engineer role
Advanced working knowledge of SQL and T-SQL programming
Experience with ETL and Data Migration
Experience with relational SQL and NoSQL databases
Experience with object-oriented/object function scripting languages: Python etc.
Experience building and optimizing data pipelines, architectures, and data sets.
Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Experience supporting and working with cross-functional teams in a dynamic environment.
Strong analytic skills related to working with unstructured datasets.
Build processes supporting data transformation, data structures, metadata, dependency, and workload management.
A successful history of manipulating, processing, and extracting value from large, disconnected datasets.
Experience with Machine Learning is a great plus
Experience with Google Cloud Platform and Snowflake is a must
Experience with message queuing and stream processing such as Pub-Sub, Azure Event Grid, Kafka etc.
Experience with data pipeline and workflow management tools such as Airflow, etc.