Data Engineer III – Data Operations & Infrastructure

ICW Group•Lehi, UT

1d•$121,625 - $217,711

About The Position

The Data Engineer III designs, develops, and implements data pipelines, data integration, and data storage solutions, including data warehouses, data lakes, and relational and non-relational databases. The role partners with IT Managers, Enterprise Business Intelligence, Data Governance, and Data Science teams to solve significant business data problems and enable data-driven decision-making, automation, and optimization. It may also serve as a Data Advisory member for Actuarial, Business Intelligence, Data Science, and Data Engineering teams, and contribute to Enterprise Data Architecture. The position helps ensure that data delivery architecture is consistent across projects. The role builds and leads scalable, reliable, efficient, testable, and maintainable data solutions and pipelines, including batch and streaming processes across traditional and cloud platforms. It analyzes complex data elements and systems; develops data models and ETL/ELT mappings; supports data warehouses; validates and optimizes data sources; and helps advance data governance, quality, architecture, tooling, and engineering practices. The Data Engineer evaluates new technologies and collaborates across teams to deliver data solutions aligned with business needs and governance requirements. This position has no supervisory responsibilities, but may mentor or train colleagues and act as a resource for team members. It operates in a professional office environment and frequently uses a computer, telephone, copier, and other standard office equipment. The employee may be required to work additional hours as needed.

Requirements

Bachelor's degree in Computer Science, Applied Mathematics, Engineering, or another technology-related field, or an equivalent combination of education and experience.
At least 6 years of experience in a data integration engineering role involving cloud or traditional environments.
Expertise in database and data warehouse design, including technologies such as MS-SQL and PostgreSQL.
Knowledge of database schema modeling and design for read and write performance.
Working knowledge of API- or stream-based data extraction processes.
Experience conducting root cause analysis on internal and external data and processes to answer business questions and identify improvement opportunities.
Background in data modeling and performance tuning in relational and NoSQL databases.
Experience with data technologies such as Hadoop, Spark, Kafka, streaming technologies, Python, Scala, or Talend.
Knowledge of data movement tools such as SSIS, Profisee, Alteryx, or Informatica.
Working knowledge of data management domains including modeling, integration, warehousing, quality, security, and governance.
Self-starter able to work in a fast-paced, changing environment, tolerate ambiguity, solve problems, and work with limited supervision.
Strong analytical and time-management skills; self-motivated and able to manage tasks with minimal supervision.
Organized and detail-oriented, able to multitask, work under pressure, and deliver under tight deadlines and changing priorities.
Ability to collaborate across teams and provide value-added solutions to meet objectives.
Strong verbal and written communication skills.
Ability to work additional hours as required.

Nice To Haves

Experience with data practices such as security, data management, and governance.
Experience in operations research, machine learning, or optimization.
Insurance experience.
Data architecture or data engineering certification.
AWS Cloud Practitioner certification or a more advanced AWS certification.
DAMA certification.
Experience with AWS technologies such as Redshift, S3, EC2, Glue, EMR, Kinesis, Lambda, and DynamoDB.
Experience with modern data architectures and platforms such as Snowflake and Databricks.
Ability to motivate and inspire a team for maximum performance.

Responsibilities

Build data solutions that support data integrity and information usability for enterprise-wide digital solutions and decision-making.
Lead the design and development of scalable batch and/or streaming data pipelines and solutions using traditional and cloud platforms.
Analyze complex data elements, systems, and data flows; develop conceptual, logical, and physical data models; and verify and implement ETL/ELT mappings and transformation logic.
Design, support, and peer review data models and schemas for new and existing data warehouse sources.
Conduct unit, integration, and system testing to validate data against source systems, and optimize query performance and cost.
Build infrastructure for extracting, transforming, and loading data from a wide variety of sources using big data technologies and tools.
Lead data initiatives to keep pipelines reliable, efficient, testable, and maintainable.
Analyze the current environment to identify data deficiencies, gaps, and opportunities.
Improve engineering team processes through data architecture, engineering, testing, and operational excellence practices.
Collaborate on data strategy components, including Master Data Management and data virtualization.
Partner with teams to deliver data solutions and work with BI and Data Science teams to implement data streams.
Coordinate with Enterprise Architecture, Technology, and Project teams to promote consistent solutions while meeting data governance requirements.
Contribute to data governance and quality practices, including design reviews, unit testing, code reviews, and continuous integration and deployment.
Work with data architects to ensure physical model outputs meet needs for data definition, structure, content, and usage.
Evaluate and conduct proof-of-concept work on technologies for the next-generation data platform ecosystem.
Collaborate with Enterprise Architecture to advance tools and standards that improve data engineering productivity and output quality.
May mentor or train team members and act as a resource for colleagues.