Lead Data Engineer - Technology Data Product

• Data Pipeline Engineering: Design, build, and support production batch and incremental pipelines using Python, SQL, and Cloud Composer to collect and process data and telemetry from enterprise tools and source systems.
• BigQuery Engineering & Optimization: Implement and maintain BigQuery datasets, tables, views, and transformation workloads. Optimize partitioning, clustering, query performance, access, storage, and compute costs.
• Data Transformation & Testing: Write and deploy automated transformation workflows and data quality checks using Dataform.
• CI/CD & Infrastructure as Code: Create and maintain repeatable Terraform configurations and GitLab CI/CD pipelines to automate testing, credential handling, and environment deployments across development, test, and production.
• System Integration & Security: Build secure integrations with enterprise APIs and source systems using appropriate authentication and credential management, incorporating retry, error handling, and rate-limiting patterns.
• Data Quality & Reliability: Configure pipeline monitoring, alerting, schema validation, and reconciliation jobs.
• Data Product Delivery: Partner with the Data Product Owner, data modelers, architects, and platform teams to translate product requirements, metric definitions, data-quality expectations, and governance requirements into reliable technical solutions.
• Documentation & Governance: Maintain technical documentation, lineage, data contracts, implementation guides, and evidence needed to support governance and audit requirements.
• Technical Mentorship & Standards: Conduct code reviews, establish reusable coding templates, and mentor team engineers on best practices for data engineering and version control.

• SQL & BigQuery: Advanced SQL proficiency with hands-on BigQuery experience managing datasets, partitioning, clustering, views, schema updates, access controls, and query optimization.
• Data Pipeline & Orchestration: Proven experience designing and running production ELT/ETL pipelines, state management, incremental loading, and workflow scheduling with Cloud Composer or Apache Airflow.
• Programming & Scripting: Strong hands-on programming skills in Python and Bash shell scripting for container and pipeline workflows.
• API Ingestion: Practical experience extracting data from enterprise REST or GraphQL APIs, handling pagination, rate limits, OAuth flows, and integration with secret management systems.
• Dataform / SQL Transformation: Hands-on experience developing modular data transformations, dependencies, and automated test assertions using Dataform or comparable SQL-based frameworks.
• Terraform & GCP Infrastructure: Experience writing reusable Terraform modules to provision GCP data and IAM resources, including BigQuery, Cloud Storage, service accounts, Cloud Run, and Cloud Composer.
• GitLab CI/CD: Practical experience maintaining GitLab CI/CD pipelines, including environment stages, automated testing, container builds, and branch management.
• Data Quality & Production Reliability: Experience implementing automated data-quality controls, reconciliation, observability, alerting, failure recovery, and production support.
• Security & Governance: Experience applying access controls, secure credential handling, lineage, auditability, and data-governance requirements within a regulated or highly governed environment.

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

• Education: Bachelor's degree in Computer Science, Information Technology, Data Engineering, or a related technical discipline, or equivalent practical experience.
• Relevant Experience: 5+ years of hands-on data engineering experience building production cloud data pipelines and data warehouse solutions.
• DevOps / CI/CD Experience: 2+ years of practical experience integrating automated testing, infrastructure as code, and CI/CD deployment workflows.

 Core Competencies

• All KeyBank employees are expected to demonstrate Key’s Values and abide by Key’s Code of Conduct.

• General Office - Prolonged sitting, ability to communicate face to face in person or on the phone with teammates and clients, frequent use of PC/laptop, occasional lifting/pushing/pulling of backpacks, computer bags up to 10 lbs.

Work Location Category

• Hybrid (2+ days)

COMPENSATION AND BENEFITS

Please click here for a list of benefits for which this position is eligible.

Key has implemented an approach to employee workspaces which prioritizes in-office presence, while providing flexible options in circumstances where roles can be performed effectively in a mobile environment.

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