Data Engineer: Real-Time Data Pipelines & Analytics

A-dec Inc.

Newberg (OR)

On-site

USD 90,000 - 120,000

Full time

14 days+

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Job summary

A-dec in Newberg, OR is seeking a Data Engineer to expand and optimize the data pipeline architecture and data warehouse. You will support software developers, data architects, and analysts across multiple teams to improve data flow and accessibility.

Responsibilities include building real-time and batch data integrations, writing complex SQL queries, and designing scalable ETL processes. Collaboration with cross-functional teams is essential to deliver reliable analytics and insights for the

Qualifications

Bachelor’s degree in Computer Science or related technical field or equivalent practical experience.
2+ years of experience in Information Technology with a focus on Business Applications, Data Engineering, Data Architecture, Data Analytics and Science.
Good understanding of data warehousing and Business Intelligence environments.
Excellent problem solving, critical thinking, and communication skills.
Experience with distributed source code version control system (e.g. git).
Experience with ticketing system (ServiceNow; JIRA etc.).
Expertise with Python, databases, and SQL expertise.
Strong analytic skills related to working with unstructured datasets.
Experience with data pipeline and workflow management tools.

Responsibilities

Build both real-time and batch data integrations from disparate source systems into the EDW.
Work with data and analytics experts to strive for superior functionality in our data systems.
Write complex queries for reporting and the application consumption.
Assemble large, complex data sets that meet functional and non-functional business requirements.
Design and implement real-time ETL and batch processing of information to support modeling and reporting needs.
Provide production support for data integration and transformation pipelines.
Scale and tune data pipelines, databases, and SQL queries.
Build and maintain data reconciliation processes.
Identify trends in data sets and develop algorithms to make raw data more useful.
Connect data to users in mixed cloud and on-prem environments.
Improve data reliability, efficiency, and quality.
Partner with data architect & analysts to support modeling and reporting needs and to build and maintain internal data processing and visualization.
Translate requests into replicable analytic reports using varying applications.
Create tools to serve data such as APIs and packages.
Troubleshoot and provide root cause analysis for database software and data integrity issues