Senior Manager Software Engineering

Job Summary

We are seeking a Senior Manager Data Engineering to lead our data engineering team and build a modern, AI-ready data ecosystem on the Azure Data Platform. This role will ensure that our enterprise data is integrated, enriched, compliant, and readily consumable for analytics, BI, and ML/AI use cases. The ideal candidate is a hands-on leader who can bridge engineering excellence with business needs, while enabling scalable AI-driven innovation.

Team Leadership & Strategy

• Lead, mentor, and grow data engineers and BI engineers, fostering a high-performance, collaborative culture.
• Define and execute the data engineering roadmap aligned with enterprise AI and analytics strategies.
• Collaborate with cross-functional teams (product, finance, IT security, operations) to ensure data priorities support business outcomes.

Data Platform & AI/ML Readiness

• Architect and oversee ingestion, transformation, and integration of diverse data sources (structured/unstructured, batch/streaming).
• Partner closely with ML/AI teams to understand data requirements, design AI-ready datasets, and ensure data pipelines support model training and inference.
• Enable CI/CD integration of ML models into production data pipelines and APIs.
• Build domain-oriented, enriched datasets that power analytics, operational insights, and advanced AI/ML use cases.

Key Tasks & Responsibilities (Essential Functions)

Data Governance & Compliance

• Oversee the data marketplace / catalog to ensure discoverability, secure access, and auditability.
• Implement access controls, encryption, and audit trails for PII/SPII and regulated data.
• Ensure adherence to SOX, GDPR, CCPA and internal compliance standards.
• Drive data quality, lineage, and governance initiatives using enterprise data marketplace tool.

Data Democratization, Consumption & Literacy

• Drive data democratization by making curated, governed data easily accessible to business users and power users.
• Partner with BI and analytics teams to build semantic layers and data models that accelerate consumption in Power BI and other tools.
• Lead data literacy programs to ensure stakeholders “speak data” and use a consistent, trusted source of truth across the organization.
• Evangelize data sharing internally and externally, making it seamless for partners, vendors, and agencies while ensuring compliance and governance.
• Champion the use of data to enable timely, actionable insights that directly impact decision-making and business outcomes.

Other

Operational Excellence & DataOps

• Monitor performance, optimize cost, and drive reliability across pipelines, Lakehouse, and warehouse environments.
• Champion DataOps practices including automation, CI/CD pipelines, testing, and monitoring.
• Collaborate with Enterprise Architects to align with enterprise-wide standards and future-state data architecture.
• Partner with Finance to implement FinOps practices that balance scalability with cost efficiency.

Innovation & Vendor Management

• Stay ahead of emerging trends (GenAI, data mesh, real-time APIs, external data monetization) and recommend adoption where relevant.
• Evaluate, select, and manage external vendors, tools, and technologies to strengthen the data ecosystem.
• Drive innovation and experimentation while ensuring long-term platform stability and governance.

Education (minimum education required)

Preferred Education (i.e. type of degree)

Skills and Minimum Experience Required

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
• 10+ years of experience in data engineering with at least 2+ years in a leadership role.
• Strong expertise in Databricks (PySpark/SQL), Azure Data Factory, and Data Lakehouse architectures.
• Proven experience enabling ML/AI use cases (model data pipelines, MLOps integration, feature store design).
• Hands-on knowledge of Power BI datasets and semantic models for analytics.
• Familiarity with compliance frameworks (SOX, GDPR, CCPA) and governance practices.
• Strong communication, stakeholder management, and cross-functional leadership skills.

Preferred Skills and Experience

• Experience with Azure ML, MLflow, or Databricks ML pipelines.
• Exposure to data marketplace/data product strategies.
• Knowledge of real-time streaming technologies (Nifi, Event Hubs, Delta Live Tables, Azure Stream Analytics).
• Experience with data monetization or external API-based data sharing models.

Physical Requirements

Travel Requirements