Sr. Data Engineer

📋 Description

• • Design, build, and maintain scalable, reliable data pipelines using Python, SQL, Spark, Databricks, and Structured Streaming to process high-volume transactional data from retail and commerce loyalty platforms.
• • Architect and deploy data products that transform raw operational data into actionable business insights, directly supporting customer experience enhancement and strategic decision-making.
• • Collaborate closely with Application Development teams, Customer Support, and Business SMEs to understand data requirements and translate them into robust engineering solutions.
• • Implement and optimize data modeling practices for both relational (PostgreSQL) and NoSQL (DynamoDB) databases to ensure data integrity, performance, and scalability.
• • Integrate AI/ML models into production data pipelines to enable predictive analytics, personalization, and automated insights across the loyalty and commerce ecosystem.
• • Leverage LLMs and coding agents such as Claude Code, Cursor, GitHub Copilot, MCP, and Agent Orchestrators to automate routine tasks, accelerate development cycles, and enhance team productivity through internal tooling and harnesses.
• • Configure and manage CI/CD pipelines using CircleCI, DroneCI, GitHub Actions, and Spacelift to ensure automated testing, validation, and deployment of data workflows.
• • Monitor data pipeline health and performance using DataDog for real-time observability, alerting, and root-cause analysis of data quality issues.
• • Implement and enforce data governance practices including privacy, security, access controls, and compliance protocols aligned with industry standards and regulatory requirements.
• • Deploy and manage cloud-based data infrastructure on AWS or Azure, leveraging native services for storage, processing, and orchestration of large-scale datasets.
• • Own end-to-end lifecycle of data engineering initiatives from requirements gathering and design through deployment, monitoring, and iterative improvement.
• • Stay current with emerging trends in data engineering, AI/ML integration, and LLM-driven development, and actively propose and pilot new technologies to elevate team capabilities.
• • Champion agentic engineering practices by building tooling that enables the broader engineering team to automate repetitive work, reduce manual intervention, and increase velocity without compromising reliability.
• • Ensure seamless version control and collaboration using Git and GitHub across cross-functional teams, maintaining clean, documented, and auditable codebases.
• • Participate in technical design reviews, code reviews, and sprint planning to align data infrastructure goals with product roadmap and customer needs.
• • Support data-driven decision-making by providing clean, accessible, and well-documented datasets to non-technical stakeholders across marketing, operations, and customer success.
• • Contribute to the evolution of PAR’s Unified Customer Experience platform by ensuring data flows are optimized across point-of-sale, digital ordering, loyalty programs, and back-office systems.
• • Document data lineage, schema definitions, and pipeline dependencies to enable transparency, auditability, and knowledge sharing across engineering and analytics teams.
• • Proactively identify bottlenecks in existing data systems and lead efforts to refactor, scale, or replace legacy components with modern, cloud-native architectures.
• • Maintain a strong focus on data quality, latency, and accuracy, ensuring that insights derived from pipelines are trustworthy and timely for business users.