Senior Manager Data Engineering

Acuity Inc.•Atlanta, GA

33d

About The Position

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.This role is open across the United States; however, candidates located in the Eastern or Central Time Zones are strongly preferred to optimize collaboration with cross-functional teams.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.
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.
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.