Data Engineer (Contract)

ArenaNet

1d•$54 - $80•Hybrid

About The Position

ArenaNet is seeking a Data Engineer for a 6-month contract role. The primary focus will be on building and maintaining reliable, readable, and efficient database views to support analytics and reporting. The role involves working with Amazon Redshift and Databricks, utilizing an internal system called DELOS for defining and deploying database logic. Training on DELOS will be provided. The company emphasizes diversity and encourages applications even if not all qualifications are met.

Requirements

Bachelor’s degree in Computer Science or a related field, or equivalent professional experience.
2+ years of hands-on experience as a Data Engineer or in a similar data-focused role.
Strong SQL skills, including joins, common table expressions (CTEs), aggregations, subqueries, and window functions.
Ability to read unfamiliar SQL, trace business logic, identify unnecessary complexity, and refactor queries.
Working knowledge of relational data modeling, schemas, views, data types, and null handling.
Experience with a cloud data warehouse or Lakehouse platform; Redshift or Databricks experience a plus.
Comfort using Git or another version-control system in a collaborative development workflow.
Ability to learn internal frameworks, follow established patterns, and clarify requirements.
Good communication skills, including the ability to explain query logic to non-technical audience.
Self-management skills and the ability to balance multiple assigned priorities in a contract environment.

Nice To Haves

Basic Python familiarity for reading utilities, tests, or supporting data-engineering code a plus.
Previous experience in the game industry.

Responsibilities

Create, update, test, and document database views in Amazon Redshift and Databricks.
Use DELOS to define and maintain database logic that can be deployed consistently across platforms.
Review existing SQL and simplify or refactor complex queries while preserving intended business logic.
Troubleshoot query failures, unexpected results, schema differences, and platform-specific SQL behavior.
Validate data outputs by comparing results and partnering with analysts to confirm downstream usability.
Follow established development, code review, version control, testing, naming, and deployment practices.
Communicate progress, blockers, assumptions, and data-quality concerns.
Support other related data engineering needs as they arise.