Data Platform Engineer

JACK Entertainment•Cleveland, OH

3d•$65,000 - $85,000

About The Position

You will have the opportunity to work across several areas of data engineering rather than being limited to one narrow specialty. This may include data integration, pipeline development, data modeling, quality monitoring, documentation, reporting support, and architecture discovery. We are more interested in how you approach problems than whether you have already used every technology in our environment. The right person is dependable, asks thoughtful questions, follows work through to completion, and knows how to use modern AI development tools responsibly and effectively.

Requirements

Strong foundational SQL skills and experience working with relational data.
Working knowledge of Python, R, or another language used for data processing and automation.
Some experience building, modifying, or supporting ETL/ELT pipelines.
Familiarity with databases, data warehouses, APIs, scheduled jobs, or cloud-based data services.
Basic experience with Git and modern software-development practices.
An ability to investigate unfamiliar problems, test assumptions, and communicate findings clearly.
A dependable approach to execution, documentation, and follow-through.
Genuine curiosity and a willingness to learn technologies outside your current experience.
Practical experience using AI coding assistants or language models as part of a development workflow.
Good judgment about validating AI-generated code and protecting confidential or sensitive company information.

Nice To Haves

Cloud platforms such as Azure, AWS, or Google Cloud.
Data platforms such as Snowflake, Databricks, BigQuery, Redshift, Microsoft Fabric, or Synapse.
Transformation or orchestration tools such as dbt, Airflow, Dagster, Prefect, or Azure Data Factory.
Business intelligence tools such as Power BI, Tableau, or Looker.
CI/CD, infrastructure as code, containers, or automated deployment processes.
Data quality, governance, lineage, cataloging, privacy, or access management.
Supporting or documenting an existing data architecture.

Responsibilities

Build, maintain, and improve pipelines that move data between operational systems, databases, warehouses, and reporting tools.
Help document our existing data environment, including data sources, integrations, transformations, storage, reporting dependencies, and access controls.
Support an audit of the current data architecture by investigating how systems connect and identifying potential reliability, scalability, data-quality, security, or maintainability concerns.
Work with more experienced team members and business stakeholders to evaluate and prioritize improvements.
Develop and maintain reusable data models for reporting, analytics, and operational use.
Add testing, monitoring, alerting, logging, and documentation to important data workflows.
Investigate failed jobs, inconsistent metrics, and other data issues through structured root-cause analysis.
Write readable, maintainable code using source control, code review, automated testing, and repeatable deployment practices.
Translate business needs into practical technical solutions with guidance from internal stakeholders.
Use AI-assisted development tools to accelerate research, coding, testing, documentation, and troubleshooting.
Review and validate AI-generated work rather than accepting it without verification.
Learn unfamiliar systems and technologies as needed and share what you discover with the team.