Lead Data Engineer - Technology Data Product

KeyBank • Brooklyn, OH

3d • $80,000 - $150,000 • Hybrid

About The Position

The Lead Data Engineer supports the design, implementation, maintenance, and lifecycle of enterprise data products, beginning with a DevOps Data Product that transforms technology, operational, and business data into trusted, governed, and reusable insights. This hands-on technical leadership role partners with Data Product Owners, data modelers, data architects, platform engineers, technology teams, and business stakeholders to develop scalable data pipelines, reusable data models, cloud-native services, and deployment automation on Google Cloud Platform.

The role focuses on solving data and platform challenges, improving data quality and reliability, reducing friction in data delivery and pipeline lifecycles, and building standardized solutions that encourage adoption. It also offers the opportunity to influence data engineering standards, strengthen platform capabilities, and help shape an ecosystem for speed, quality, governance, and resilience.

Requirements

• Advanced SQL proficiency and hands-on BigQuery experience, including managing datasets, partitioning, clustering, views, schema updates, access controls, and query optimization.
• Proven experience designing and operating production ELT/ETL pipelines, state management, incremental loading, and workflow scheduling with Cloud Composer or Apache Airflow.
• Strong hands-on Python programming and Bash shell scripting skills for container and pipeline workflows.
• Practical experience extracting data from enterprise REST or GraphQL APIs, handling pagination, rate limits, OAuth flows, and secret-management integrations.
• Hands-on experience developing modular transformations, dependencies, and automated test assertions with Dataform or comparable SQL-based frameworks.
• Experience writing reusable Terraform modules to provision GCP data and IAM resources, including BigQuery, Cloud Storage, service accounts, Cloud Run, and Cloud Composer.
• Practical experience maintaining GitLab CI/CD pipelines, including environment stages, automated testing, container builds, and branch management.
• Experience implementing automated data-quality controls, reconciliation, observability, alerting, failure recovery, and production support.
• Experience applying access controls, secure credential handling, lineage, auditability, and data-governance requirements in a regulated or highly governed environment.
• Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a related technical discipline, or equivalent practical experience.
• At least 5 years of hands-on data engineering experience building production cloud data pipelines and data warehouse solutions.
• At least 2 years of practical experience integrating automated testing, infrastructure as code, and CI/CD deployment workflows.
• Demonstrate Key’s Values and abide by Key’s Code of Conduct.

Nice To Haves

• Experience packaging and running lightweight workloads using Docker, Cloud Build, and Cloud Run.
• Prior experience building reporting data models around software engineering tools such as Jira, ServiceNow, or GitLab.
• Familiarity with event-driven data ingestion using Pub/Sub or Apache Beam / Google Cloud Dataflow.
• Familiarity with engineering, operational, productivity, quality, cost, risk, or AI-related data.

Responsibilities

• Design, build, and support production batch and incremental data pipelines using Python, SQL, and Cloud Composer to collect and process enterprise tool and source-system data and telemetry.
• Implement and maintain BigQuery datasets, tables, views, and transformation workloads; optimize partitioning, clustering, query performance, access, storage, and compute costs.
• Develop and deploy automated transformation workflows and data-quality checks using Dataform.
• Create and maintain reusable Terraform configurations and GitLab CI/CD pipelines to automate testing, credential handling, and deployments across development, test, and production environments.
• Build secure integrations with enterprise APIs and source systems, using appropriate authentication and credential management, retry and error handling, and rate-limiting patterns.
• Configure pipeline monitoring, alerting, schema validation, and reconciliation jobs.
• Work with Data Product Owners, data modelers, architects, and platform teams to turn product requirements, metric definitions, data-quality expectations, and governance requirements into reliable technical solutions.
• Maintain technical documentation, lineage, data contracts, implementation guides, and evidence supporting governance and audit requirements.
• Conduct code reviews, establish reusable coding templates, and mentor engineers on data engineering and version-control best practices.

Benefits

• Eligible for incentive compensation, which may include production, commission, and/or discretionary incentives.
• Benefits are available to eligible employees; specific benefits are not listed in the posting.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior