Senior Data Engineer

CINC Systems; United States; Hybrid; USD 140,000 - 190,000; Full time

Job summary

CINC Systems is seeking a Senior Data Engineer to design and build scalable data pipelines powering analytics, automation, and applied AI across our platform. You will architect data flows between transactional systems, data lakes, and AI-powered applications.

You will collaborate with AI engineers, product teams, and architects to ensure data accessibility, reliability, and support for intelligent features. This senior role emphasizes ownership, practicality, and impact.

Qualifications

• 8+ years of experience in data engineering or backend software engineering focusing on large-scale data systems.
• Advanced proficiency in SQL and one or more 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 (SQL Server, Postgres, Redshift).
• Experience building ETL/ELT pipelines with data modeling, versioning, and testing practices.
• Familiarity with AI/ML data patterns including embeddings, feature stores, and RAG pipelines.
• Knowledge of API-based data access, GraphQL/REST API design, and DevOps for data engineering.
• Understanding of data governance, access control, and privacy best practices.

Responsibilities

• Design and build robust, scalable data pipelines for analytics, APIs, and AI applications.
• Architect and maintain modern data infrastructure across cloud environments (AWS) with Lambda, Glue, Athena, EventBridge, and S3.
• Collaborate with AI and product teams to provide high-quality data for training, inference, and real-time decisions.
• Develop and maintain data models and schemas optimized for analytics and operations.
• Define data contracts and governance for data lineage, versioning, and reliability across microservices and AI systems.
• Build streaming and event-driven architectures supporting low-latency data flows.
• Implement data quality automation and observability to detect anomalies and pipeline health issues.
• Work with Product and Analytics to define KPIs and data pipelines measuring business impact.
• Collaborate with AI engineers to integrate embedding pipelines, RAG data sources, and feature stores.
• Lead by hands-on example, perform code reviews, and set data engineering standards.

Skills

SQL proficiency

Python

TypeScript

Java

Data modeling

DevOps

Mentoring

Tools

AWS

Glue

DynamoDB

S3

GraphQL

REST API

Job description

About CINC Systems

CINC Systems is the leading provider of accounting and management software for the community association management industry. Ou platform powers over 50,000 associations and 6 million homes, connecting property managers, boards, and residents through secure reliable, and intelligent technology.

We are modernizing our platform into an AI-enabled, modular ecosystem that connects financial data, operations, and user experience in new ways. The data foundation is critical to that mission. We are looking for a Senior Data Engineer who blends strong data engineering skills with applied AI knowledge to design systems that deliver accurate, reliable, and intelligent insights at scale.

About The Role

The Senior Data Engineer is a hands‑on technical leader responsible for designing and building data systems that power analytics, automation, and applied AI across the CINC ecosystem.

You will architect, build, and optimize pipelines that move data seamlessly between transactional systems, data platforms, and AI‑powered applications. You will work closely with AI engineers, product teams, and architects to ensure that data is accessible, reliable, and designed for intelligent applications.

This role is ideal for a technically strong engineer who is passionate about transforming complex data into valuable, actionable intelligence. You combine the rigor of a systems engineer with the curiosity of a data scientist and the mindset of a product builder.

• 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

Location

Worldwide (Remote/Hybrid)

Qualifications

Technical Expertise

• 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

Applied AI and Product Thinking

• 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

Collaboration and Leadership

• 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

Mindset and Values

• 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

What Success Looks Like

• Data systems are highly reliable, observable, and integrated with the AI and product ecosystem
• Product and AI teams can access clean, well‑structured data with minimal friction
• Data flow is event-driven, resilient, and supports real‑time decision‑making
• AI‑enabled features deliver measurable business and user impact because of data reliability and clarity
• The Senior Data Engineer is recognized as a multiplier, improving both the data platform and the engineering culture

CINC is an Equal Opportunity Employer of women, minorities, protected veterans and individuals with disabilities.