Note: The job is a remote job and is open to candidates in USA. CINC Systems provides accounting and management software for the community association management industry. The Senior Data Engineer will design, build, and optimize scalable data systems and pipelines that support analytics, automation, APIs, and applied AI, while partnering with engineering, product, and analytics teams to ensure data reliability and usability.
Responsibilities
- Design and build robust, scalable, and secure data pipelines to collect, transform, and serve data for analytics, APIs, and AI applications
- Architect and maintain modern data infrastructure across cloud environments (AWS preferred) using services such as Lambda, Glue, Athena, EventBridge, and S3
- Partner with AI and application engineering teams to provide structured, high-quality data for training, inference, and real-time decision systems
- Develop and maintain data models and schemas optimized for both analytics and operational use
- Design data contracts and governance patterns that ensure data lineage, versioning, and reliability across microservices and AI systems
- Build streaming and event-driven data architectures that support low-latency, high-integrity data flows
- Implement data quality automation and observability systems that detect anomalies and validate pipeline health
- Work with Product and Analytics to define KPIs, metrics, and usage data pipelines that help measure business impact
- Collaborate with AI engineers to integrate embedding pipelines, RAG (retrieval-augmented generation) data sources, and feature stores for intelligent applications
- Drive continuous improvement in data engineering practices through code reviews, pairing, and knowledge sharing
- Participate in architecture reviews and contribute to broader engineering standards around data security, compliance, and scalability
- Leverage AI-native tools and techniques to improve data classification, anomaly detection, and metadata enrichment
Skills
- 8+ years of experience in data engineering or backend software engineering with a strong focus on large-scale data systems
- Advanced proficiency in SQL and one or more programming languages such as Python, TypeScript, or Java
- Experience designing and operating event-driven data architectures and microservices using AWS services (EventBridge, S3, Lambda, API Gateway, DynamoDB, Glue)
- Strong understanding of relational and analytical databases including SQL Server, Postgres, or Redshift
- Experience building and maintaining ETL and ELT pipelines with strong data modeling, versioning, and testing practices
- Familiarity with AI and ML data patterns including embeddings, feature stores, and RAG pipelines
- Knowledge of API-based data access and GraphQL or REST API design principles
- Experience applying DevOps principles to data engineering including CI/CD pipelines, IaC, and observability
- Understanding of data governance, access control, and privacy best practices
- Experience supporting AI and ML applications in production, including integration with APIs like OpenAI, Anthropic, or Bedrock
- Practical understanding of how data quality and architecture affect AI outcomes and product experiences
- Ability to design pipelines that deliver data optimized for model training, fine-tuning, and real-time inference
- Experience working with vector databases such as Weaviate, Pinecone, or Postgres pgvector
- Skilled at identifying opportunities to use automation and AI to improve data engineering workflows
- Hands-on engineer who leads by doing, enabling others through clarity and communication
- Excellent communicator able to bridge the gap between engineering, product, and analytics teams
- Structured thinker with the ability to diagnose system constraints and simplify complex data flows
- Collaborative mindset with strong ownership and a focus on measurable business impact
- Comfortable mentoring others and setting standards for data craftsmanship across teams
- Believes that AI is an amplifier of strong fundamentals and that reliable, well-designed data systems are the foundation of intelligence
- Operates with a builder's mindset, focused on outcomes, not tools
- Customer-obsessed, motivated by delivering insights and experiences that make a difference in users' lives
- Embraces continuous improvement, experimentation, and learning
- Values simplicity, reliability, and transparency in both systems and communication
- Balances innovation with stability, knowing that smooth is fast
- Architect and maintain modern data infrastructure across cloud environments (AWS preferred) using services such as Lambda, Glue, Athena, EventBridge, and S3
Benefits
- Worldwide (Remote/Hybrid)
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