Description

Lead the design and implementation of enterprise data platforms, modernizing legacy environments to cloud-native architectures while mentoring a team of data engineers and architects.

This role is on-site.

Responsibilities

• Define and drive the enterprise data engineering strategy, roadmap, and architecture standards for scalable, secure data platforms.
• Design and maintain high-volume batch, streaming, and event-driven data pipelines using cloud technologies and modern data architectures.
• Lead, mentor, and manage a team of Data Engineers, Data Architects, and Integration Specialists, establishing technical standards and best practices.
• Implement data governance, quality frameworks, and observability processes to ensure compliance, reliability, and trust in enterprise data assets.
• Manage delivery of concurrent data initiatives, driving agile practices, risk mitigation, and stakeholder alignment across global teams.

Required Skills

• 10+ years of experience in Data Engineering or Data Architecture, with 3+ years leading technical teams or enterprise-scale initiatives.
• Strong expertise in SQL, Python, Spark, or Scala for data processing and pipeline development.
• Hands-on experience with cloud platforms (Azure, AWS, or Google Cloud) and tools like Databricks, Snowflake, Synapse, Redshift, or BigQuery.
• Demonstrated experience designing data lakes, lakehouse architectures, and data warehouses.
• Proficiency in DataOps, CI/CD, and Infrastructure-as-Code practices to drive platform automation.
• Experience with ETL/ELT frameworks, data modeling, metadata management, and data lifecycle management.
• Bachelor's degree in Computer Science, Engineering, or related field; Master's degree preferred.

Preferred Skills

• Experience enabling AI and machine learning workloads by building optimized data environments for data scientists.
• Proven track record of leading cross-functional teams in global delivery environments.

Key Skills

Education

Bachelor's or Master's degrees