Data Engineer II

ICW Group•Lehi, UT

4d•$95,379 - $160,850•Onsite

About The Position

The Data Engineer II will design, develop, and implement data pipelines, data integration and data storage solutions such as data warehouses, data lakes, relational and non-relational databases. The role will partner closely with IT Managers, Enterprise Business Intelligence, Data Governance and Data Science teams to solve business-significant data problems and enable data-driven decision-making, automation, and optimization. Data is central to ICW Group’s business strategy and digital evolution, and the Data Engineer ensures optimal data delivery architecture is consistent throughout all projects.

Requirements

Bachelor's degree in Computer Science, Applied Mathematics, Engineering, or any other technology related field required, or equivalent combination for education and experience.
Minimum 3 years of experience in a data integration (Cloud/Traditional) engineering related role required.
Expertise with database & data warehouse design, using MS-SQL, PostgreSQL etc.
Knowledge of Model and Design of DB schemas for read and write performance.
Working knowledge of API or stream-based data extraction processes.
Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
Experience with AWS technologies like Redshift, S3, EC2, Glue, EMR, Kinesis, Lambda, DynamoDB, etc.
Experience with modern data architectures and modern data platforms like Snowflake, Databricks etc.
Experience with data technologies: Hadoop, Spark, Kafka, Spark & Kafka Streaming, Python, Scala, Talend etc.
Knowledge and experience with data movement tools – SSIS, Profisee, Alteryx, Informatica.
A self-starter mentality that thrives in a rapidly changing, fast-paced environment and tolerates ambiguity while demonstrating problem-solving.
Strong analytical and time management skills.
Self-motivated and able to handle tasks with minimal supervision.
Must be organized, detail oriented, and able to multi-task.
Ability to work well under pressure and deliver results with tight deadlines and under changing priorities.
Ability to cross collaborate with multiple teams and offer value-added solutions to meet objectives.
Strong verbal and written communication skills.

Nice To Haves

Insurance experience a plus.
Data architecture or data engineering related certifications strongly desired.
AWS Cloud Practitioner or more advanced AWS certification preferred.

Responsibilities

Builds data solutions that ensure data integrity and information usability for enterprise-wide digital solutions and decision making.
Creates and maintains scalable data pipelines and solutions (batch and/or streaming) that make the best use of traditional and cloud platforms (AWS or similar) by understanding the business, technology, and data landscape including real time data processing.
Provides analysis of complex data elements and systems, data flow and in development of conceptual, logical, and physical data models as well as verification and implementation of ETL/ELT mappings and transformation logic.
Designs, supports and peer reviews the data models and schemas for new and existing data sources for the data warehouse.
Conducts unit, integration, and system tests on our data sources to validate data against source systems, and continuously optimize performance to improve query speed and reduce cost.
Builds pipelines that are reliable, efficient, testable, and maintainable.
Maintains a good understanding of the current landscape and assists in the analysis of data deficiencies, gaps, and opportunities.
Contributes to building the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using ‘big data’ technologies and tools.
Partners with various teams in implementing overall data solutions.
Works closely with BI and Data Science teams in implementing various data streams.
Collaborates with data architects to ensure the output of the physical models meet required needs— e.g., collaborates on data definition, data structure, data content and data usage.
Partners with Enterprise Architecture, Technology, and Project teams to ensure consistency of solutions approach while maintaining data governance requirements.
Contributes to data governance and data quality best practices including design reviews, unit testing, code reviews, and continuous integration and deployment.
Collaborates with the Enterprise Architecture team to drive tooling and standards to improve the productivity and quality of output for data engineers across the company.