Sr. Data Engineer

• Sr. Data Engineer
• 📍 Location: Shelton, Connecticut, United States
• 🏢 Industry: Restaurants
• 💼 Work Setting: Hybrid

Are you passionate about designing modern data platforms, building scalable data pipelines, and enabling enterprise-wide analytics through cloud-based technologies?

We are seeking an experienced Senior Data Engineer to lead the design, development, and optimization of data integration and analytics solutions that power critical business operations and decision-making.

In this role, you will collaborate with product owners, business stakeholders, and engineering teams to deliver reliable, scalable, and high-performing data pipelines. You will play a key role in supporting enterprise data initiatives, implementing data governance standards, and leveraging modern cloud data platforms to create trusted, analytics-ready data assets.

• Key Responsibilities
• Data Pipeline Development
• Design, develop, and maintain scalable, high-performance data pipelines and integrations.
• Build reliable data solutions that support both operational system integrations and enterprise analytics platforms.
• Ensure data pipelines are resilient, flexible, and aligned with enterprise architecture standards.
• System Integration & Project Delivery
• Partner with cross-functional teams to deliver enterprise integration initiatives.
• Collaborate with stakeholders to gather requirements, define technical solutions, and estimate development efforts.
• Ensure successful delivery of large-scale data and integration projects.
• Automation & DevOps
• Develop automated testing frameworks and deployment processes for data pipelines and integrations.
• Support CI/CD practices to improve deployment reliability and development efficiency.
• Contribute to continuous improvement of engineering standards and automation capabilities.
• Data Quality & Governance
• Ensure high levels of data quality, consistency, and accuracy across enterprise data platforms.
• Implement data governance, master data management, lineage tracking, and metadata management best practices.
• Conduct data analysis and troubleshooting to resolve data issues and support business needs.
• Operational Support
• Provide advanced technical support for critical data and integration solutions.
• Assist operational teams with issue resolution and root cause analysis.
• Monitor data platform performance and proactively address potential challenges.
• Documentation & Knowledge Sharing
• Create and maintain technical documentation, including data flows, lineage diagrams, architecture diagrams, and operational procedures.
• Participate in code reviews and provide mentorship to junior engineers.
• Promote engineering best practices and knowledge sharing across teams.

• Required Qualifications
• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent professional experience.
• 5–8 years of experience designing and building enterprise data pipelines and system integrations.
• Minimum 3 years of experience working within cloud-based data environments.
• Strong experience delivering scalable, production-ready data engineering solutions.
• Excellent communication and stakeholder management skills.

• Technical Skills
• Cloud Data Platforms
• Hands-on experience building modern data solutions using cloud-based data platforms such as Databricks, Snowflake, or equivalent technologies.
• Experience implementing lakehouse architectures and modern analytical data ecosystems.
• Data Engineering
• Expertise in ETL/ELT development, data ingestion, transformation, and orchestration.
• Experience developing robust batch and streaming data pipelines.
• Knowledge of modern workflow orchestration and pipeline automation tools.
• Data Architecture & Modeling
• Experience designing layered data architectures for analytics-ready data.
• Strong understanding of dimensional modeling, data vault methodologies, and analytical schema design.
• Ability to create scalable models that support reporting, analytics, and AI initiatives.
• Programming & Querying
• Strong proficiency in SQL for complex data analysis and transformation.
• Experience with Python and/or PySpark for data engineering and automation development.
• Ability to optimize data processing and improve solution performance.
• Performance Optimization
• Experience tuning large-scale data workloads and optimizing processing efficiency.
• Knowledge of partitioning strategies, query optimization, clustering techniques, and resource management.
• Governance & Security
• Familiarity with enterprise data governance frameworks, access controls, data lineage, and security best practices.
• Experience implementing governed data assets and maintaining data compliance standards.
• DevOps & CI/CD
• Experience using version control systems and modern deployment frameworks.
• Knowledge of automated testing, release management, and infrastructure-as-code principles.
• Cloud Ecosystem
• Experience working with cloud services supporting data engineering workloads across AWS, Azure, GCP, or similar environments.
• Analytics & AI Readiness
• Understanding of data platforms that support advanced analytics, machine learning, and generative AI use cases.
• Experience preparing trusted datasets for analytical and AI-driven solutions.

• Preferred Competencies
• Strong analytical and problem-solving skills.
• Ability to work effectively in cross-functional and agile environments.
• Experience mentoring junior engineers and conducting code reviews.
• Strong attention to detail and commitment to data quality.
• Ability to manage multiple priorities in a fast-paced environment.
• Excellent verbal and written communication skills.

• What Success Looks Like
• Delivering scalable and reliable enterprise data pipelines.
• Ensuring high-quality, trusted data across analytical platforms.
• Supporting critical business initiatives through modern data engineering solutions.
• Driving adoption of data governance and engineering best practices.
• Enabling advanced analytics, reporting, and AI capabilities through well-designed data architectures.