Sr. Data Engineer

Foundation Software•Strongsville, OH

About The Position

Foundation Software is seeking a hands-on Senior Data Engineer to help design, build, and establish engineering standards for its Microsoft-heavy data ecosystem. The role focuses on modernizing the data platform and delivering scalable data pipelines, data models, and platform solutions for analytics, business intelligence, AI, and enterprise reporting. The engineer will contribute across architecture and design, implementation, and production support, while helping shape how the organization builds, operates, and evolves its data environment. The position partners closely with business stakeholders, analysts, and technology teams. It also helps establish best practices for reliability, performance, governance, and deployment, and promotes technical standards and continuous improvement through mentoring and coaching. Foundation describes its culture as fun, casual, and hard-working, with an emphasis on creativity and work-life balance. Office amenities and activities include manicures and pedicures, massages, laundry service, monthly car washes, catered Wednesday lunches, monthly happy hours, a workout facility, fitness classes, free personal training, a game room, a full kitchen, coffee, soft drinks, snacks, sporting-event tickets, lunch-and-learns, and employee-appreciation events. The company states that it is an Equal Opportunity Employer.

Requirements

Bachelor's degree in Computer Science, Data Engineering, Mathematics, or a related field.
At least 7 years of hands-on experience in data engineering or a closely related discipline.
Expert-level SQL skills, including database design, dimensional modeling, query optimization, indexing, and performance tuning.
Experience with modern cloud data warehouses such as Fabric Warehouse, BigQuery, or Databricks, including Lakehouse architectures and large-scale data processing platforms.
Strong experience designing logical and physical data models, including star and snowflake schemas.
Background implementing CI/CD pipelines and automated deployment practices, such as DevOps or GitHub Actions.
Programming skills in languages commonly used in data engineering, such as Python, Java, or Scala.
Experience with modern ETL/ELT orchestration tools, such as Fivetran, Azure/Fabric Data Factory, and dbt or similar tools.
Strong experience and knowledge of cloud platforms and data services, such as AWS, Azure, or Fabric.
Proficiency with version control systems, such as GitHub or Azure DevOps.
Experience with BI tools, such as Microsoft Power BI or Tableau.
Ability to work independently in a dynamic environment and manage multiple priorities amid ambiguity or evolving requirements.

Responsibilities

Architect, build, and manage data repositories, data integration solutions, and scalable pipelines.
Implement CI/CD practices for data pipelines and transformations.
Design and implement monitoring, alerting, observability, and operational support processes for data pipelines and platforms.
Troubleshoot and resolve pipeline failures, performance bottlenecks, and production incidents.
Assist in evaluating, selecting, deploying, and maintaining data infrastructure components, including databases, data platforms, and pipeline tools.
Create, maintain, and evolve logical and physical dimensional data models and schema designs for storage, retrieval, and analytics.
Optimize data storage, processing, query performance, and cloud infrastructure costs.
Apply workload tuning, partitioning, indexing, and resource-utilization strategies.
Implement data validation, quality checks, and metadata management to monitor data accuracy, consistency, and reliability.
Ensure data governance, compliance, and security requirements are met by design as code.
Maintain proficiency with data engineering technology trends and evaluate their potential impacts.
Document data architectures, infrastructure designs, pipelines, workflows, transformations, and data models for technical and non-technical audiences.
Collaborate with business and technical stakeholders to define requirements, prioritize initiatives, and deliver trusted, scalable data products and services.
Mentor and coach team members and promote engineering best practices, technical standards, and continuous improvement.