Data Platform Engineer

JACK Entertainment•Cleveland, OH

About The Position

The Data Platform Engineer works across a range of data engineering activities, which may include data integration, pipeline development, data modeling, quality monitoring, documentation, reporting support, and architecture discovery. The role focuses on problem-solving, dependable execution, thoughtful communication, and responsible use of modern AI development tools. The engineer builds and improves data workflows, helps document and assess the existing data environment, and develops reusable data models for reporting, analytics, and operational use. Working with experienced team members and business stakeholders, the role investigates data issues, evaluates potential improvements, and translates business needs into practical technical solutions. The position also emphasizes maintainable code, testing, monitoring, documentation, and verification of AI-generated work. Candidates should have foundational SQL skills, some experience with data pipelines and relevant development practices, and approximately 1–3 years of related experience or equivalent hands-on experience. A bachelor’s degree in Computer/Data Science or Analytics is requested, with comparable professional experience accepted in lieu of a degree. The candidate must be able to obtain the specified gaming licenses.

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.
Ability to investigate unfamiliar problems, test assumptions, and communicate findings clearly.
Dependable approach to execution, documentation, and follow-through.
Curiosity and willingness to learn technologies outside 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.
Bachelor’s degree in Computer/Data Science or Analytics, or comparable professional experience in the discipline.
Approximately 1–3 years of relevant professional experience in data engineering, software development, analytics engineering, database development, or a related area; equivalent hands-on experience through internships, substantial projects, certifications, or other demonstrated work will also be considered.
Ability to obtain an OCCC Gaming License, OCCC Sports Gaming License, and an OLC Gaming License.

Nice To Haves

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

Responsibilities

Build, maintain, and improve pipelines that move data between operational systems, databases, warehouses, and reporting tools.
Document data sources, integrations, transformations, storage, reporting dependencies, and access controls in the existing data environment.
Investigate system connections and identify potential reliability, scalability, data-quality, security, or maintainability concerns as part of an audit of the current data architecture.
Work with 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, and review and validate AI-generated work.
Learn unfamiliar systems and technologies as needed and share discoveries with the team.