Senior Data Engineer

Job Description:

The Data Engineering Platform Lead is responsible for the overall design, operation, and evolution of enterprise data platforms supporting analytics, integration, and business intelligence. This role owns the data engineering platform stack-including Snowflake, Informatica, dbt, AWS, and CI/CD tooling-and serves as the primary interface between data engineering teams and partner organizations such as Cyber Security, Cloud Infrastructure, DevOps, and Enterprise Architecture. This is a hands?on technical leadership role with strong platform management and communication responsibilities, ensuring platforms are secure, scalable, cost?effective, and aligned with enterprise standards. Key Responsibilities Platform Ownership & Strategy Own the endtoend data engineering platform, including data storage, ingestion, transformation, orchestration, and DevOps tooling. Define and maintain platform standards, reference architectures, and best practices for: Data ingestion (e.g., Informatica) Data transformation and modeling (dbt) Data warehousing (Snowflake) Drive platform roadmaps and adoption of modern data engineering patterns. Evaluate and recommend platform enhancements, tooling upgrades, and new capabilities. Cross?

Functional Collaboration Act as the primary point of contact with: Cyber Security (data protection, access controls, audits) Cloud Infrastructure (AWS services, scalability, resiliency) DevOps (CI/CD, environment management, automation) Enterprise Architecture and Data Governance teams Translate data engineering needs into platform and infrastructure requirements and ensure alignment across teams. Technical Leadership Provide technical leadership and guidance to data engineers using the platform. Establish and enforce dbt standards, including: Project structure and layering Naming conventions and documentation Testing, freshness, and data quality practices Guide solution designs to ensure efficient, secure, and scalable use of Snowflake and dbt. Promote software engineering and DevOps best practices for analytics engineering DevOps & Automation Lead implementation of CI/CD pipelines for data platforms, including dbt deployments. Integrate Git?based workflows, automated testing, and controlled promotion across environments. Partner with DevOps teams to improve reliability, repeatability, and release confidence. Operational Excellence Ensure platform reliability, performance, and cost optimization. Define monitoring, alerting, and support models for platform services. Support compliance, audit, and risk remediation activities. Maintain platform documentation, runbooks, and onboarding materials. Required Technical Skills Strong experience managing and operating enterprise data platforms, including: Snowflake (architecture, administration, performance, security) Informatica (PowerCenter and/or Informatica Cloud) dbt (core concepts, modeling patterns, tests, documentation, environments) AWS (foundational services supporting data platforms) Hands?on experience with DevOps and CI/CD, including: Git / GitHub (branching and version control) Jenkins (or equivalent CI/CD tools) Strong understanding of: Data modeling and transformation best practices Cloud security and access controls Platform scalability, reliability, and cost management Communication & Leadership Skills Excellent verbal and written communication skills. Proven ability to work across multiple teams with differing priorities. Comfortable leading design reviews, architecture discussions, and platform governance forums. Ability to influence without direct authority and drive adoption of standards. Qualifications Bachelor s degree in Computer Science, Engineering, or related field. 7+ years of experience in data engineering or data platform engineering. 3+ years in a technical lead, platform lead, or principal?level role. Experience in large enterprise or regulated environments preferred. Preferred Qualifications Experience supporting federated development teams using shared dbt and Snowflake platforms. Familiarity with data governance, metadata management, and analytics catalogs.