Senior Director of Data Engineering and Transformation
New
Remote opportunity open to candidates located anywhere in the U.S.Full-TimeDirector
Salary174,876 - 256,486 USD per year
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Job Details
- Experience
- 15+ years
- Required Skills
- Data engineeringCI/CDData modeling
Requirements
- Bachelor’s degree in Computer Science, Information Technology, Engineering, Data Science, or related field (Advanced degree preferred).
- 15+ years of experience in data engineering, data architecture, enterprise data platforms, data integration, or related technical disciplines.
- Strong experience designing and implementing large-scale data architectures, modern data platforms, and enterprise data pipelines.
- Deep knowledge of ETL/ELT pipelines, data ingestion, data transformation, data modeling, data warehousing, lakehouse architectures, and cloud data systems.
- Experience structuring data for analytics, reporting, AI, machine learning, business intelligence, and automation use cases.
- Strong understanding of data quality, security, metadata, lineage, access management, and governance.
- Proven ability to lead technical teams and manage complex data engineering, architecture, and transformation programs.
- Strong communication skills with experience presenting to executives.
- Experience evaluating, selecting, and implementing data technologies and engineering frameworks.
- Familiarity with cloud-based data platforms (e.g., Azure, AWS, Google Cloud, Snowflake, Databricks, Microsoft Fabric).
- Knowledge of data mesh, lakehouse architecture, DevOps, DataOps, and CI/CD.
Responsibilities
- Transform enterprise data infrastructure by modernizing data platforms, architectures, pipelines, and engineering practices.
- Make data more available and consumable for business users, enterprise applications, analytics platforms, and artificial intelligence.
- Design scalable enterprise data architectures that support analytics, AI, automation, reporting, and operational use cases.
- Lead data pipeline development and optimization, including ETL/ELT, ingestion, transformation, orchestration, monitoring, testing, and production support.
- Drive data platform modernization across cloud, lakehouse, integration, storage, analytics, and AI-enablement capabilities.
- Deliver reusable data products and solutions, including APIs, semantic layers, governed access patterns, analytical datasets, and AI-ready data structures.
- Enable AI-ready data architecture for machine learning, intelligent applications, AI agents, advanced analytics, and automation workflows.
- Lead complex transformation initiatives from strategy through execution.
- Embed governance into technical delivery, including data quality, security, lineage, metadata, access controls, and compliance.
- Lead and mentor technical teams across data architecture, data engineering, platform engineering, and technical delivery.
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