Data Processing Manager Jobs in Connecticut (NOW HIRING)

Data Engineer

📍 Location: Shelton, CT, United States

🏢 Industry: Restaurants

💼 Work Setting: Hybrid

Are you a data engineering leader with experience building modern cloud-based data platforms, leading high-performing teams, and enabling analytics and data-driven decision making? This role offers the opportunity to shape the organization's data engineering roadmap, modernize data platforms, improve data reliability, and support enterprise analytics, BI, AI, and data products.

As the Director of Data Engineering, you will partner closely with Data Product, Analytics, Enterprise Architecture, Platform Engineering, Security, and Compliance teams to ensure trusted, scalable, and accessible data solutions across the organization.

Key Responsibilities

Data Engineering Strategy & Leadership

Data Roadmap Ownership

• Own and execute the enterprise data engineering roadmap.
• Align engineering priorities with business and data product objectives.
• Define long-term strategies for data platform modernization and scalability.
• Drive adoption of modern data engineering best practices.

Organizational Leadership

• Lead and develop managers, technical leads, and senior data engineers.
• Build a high-performing engineering culture focused on reliability, ownership, and innovation.
• Drive hiring, coaching, succession planning, and career development.

Data Platform Architecture & Delivery

Data Pipeline Engineering

Design, build, and support scalable:

• Batch Processing Pipelines
• Streaming Data Pipelines
• Data Integration Frameworks
• Data Services
• Enterprise Data Platforms

Platform Reliability

Ensure data platforms deliver:

• High Availability
• Scalability
• Performance
• Reliability
• Security

Data Lifecycle Management

Oversee:

• Data Ingestion
• Data Transformation
• Data Processing
• Data Delivery
• Data Consumption

Data Platform Modernization

Technology Transformation

• Drive modernization initiatives across data ecosystems.
• Reduce technical debt and improve maintainability.
• Evaluate and implement emerging technologies and frameworks.
• Improve operational efficiency and platform scalability.

Enterprise Standards

• Establish reusable engineering frameworks and patterns.
• Promote consistency and standardization across teams.
• Align engineering practices with enterprise architecture principles.

Data Quality, Governance & Observability

Data Quality Management

• Establish standards for enterprise data quality.
• Monitor data accuracy, completeness, and consistency.
• Improve trust in organizational data assets.

Observability & Monitoring

Develop standards for:

• Data Observability
• Monitoring
• Alerting
• Incident Response
• Root Cause Analysis

Lineage & Documentation

• Ensure traceability of critical data assets.
• Implement documentation standards.
• Enable transparency across data pipelines and platforms.

Cross-Functional Collaboration

Business & Product Alignment

Partner with:

• Data Product Managers
• Analytics Teams
• BI Teams
• Data Scientists
• Enterprise Architects
• Platform Engineering Teams

Outcome-Focused Delivery

• Translate business goals into engineering priorities.
• Deliver trusted datasets that support strategic decision-making.
• Enable self-service analytics and advanced data capabilities.

Security, Privacy & Compliance

Data Protection

Collaborate with:

• Security Teams
• Privacy Officers
• Compliance Organizations

To ensure:

• Data Security
• Regulatory Compliance
• Access Controls
• Data Governance Standards

Risk Management

• Implement secure data engineering practices.
• Ensure adherence to enterprise compliance requirements.
• Support audit and regulatory initiatives.

Performance & Operational Excellence

KPI Management

Define and monitor metrics such as:

• Pipeline Reliability
• Data Freshness
• Data Availability
• SLA Performance
• Cost Efficiency
• Platform Utilization

Service Delivery

• Establish operational excellence standards.
• Improve platform stability and resiliency.
• Drive continuous improvement initiatives.

Required Qualifications

Education

Bachelor's Degree in:

• Computer Science
• Engineering
• Data Science
• Information Technology
• Related Technical Field

Preferred

• Master's Degree or Advanced Technical Degree

Experience

Data Engineering

• 8-12 years of experience in:
• Data Engineering
• Data Platforms
• Cloud Data Infrastructure
• Analytics Engineering

Leadership

• 3-5 years of experience leading teams, managers, or enterprise-scale data engineering functions.
• Proven success managing large technical organizations.

Enterprise Data Platforms

Strong experience with:

• Cloud Data Platforms
• Distributed Systems
• Enterprise Data Architectures
• Large-Scale Data Processing

Technical Skills

Data Engineering

• Data Pipelines
• ETL / ELT
• Batch Processing
• Streaming Architectures
• Data Integration

Cloud Technologies

• Cloud Data Platforms
• Distributed Computing
• Enterprise Data Ecosystems
• Data Platform Operations

Architecture & Governance

• Data Modeling
• Data Governance
• Data Quality
• Data Lineage
• Data Observability

Analytics Enablement

• Business Intelligence
• Analytics Platforms
• Data Warehousing
• Data Science Enablement

Key Skills

• Data Engineering Leadership
• Data Platform Strategy
• Cloud Data Platforms
• Data Architecture
• ETL/ELT
• Data Pipelines
• Streaming Data
• Data Governance
• Data Quality
• Platform Modernization
• Analytics Enablement
• Team Leadership