Senior Data Engineer

🔥 0 minutes ago

🌏 Anywhere in the World

⏰ Full Time

🟠 Senior

🚰 Data Engineer

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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CINC Systems

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2005

☁️ SaaS

🏠 Real Estate

🤝 B2B

💰 Private Equity Round - CINC Systems on 2023-12

SaaS • Real Estate • B2B

CINC Systems is a cloud-based, all-in-one software platform for community association management (HOAs and COAs) that helps management companies, boards, and homeowners streamline operations and improve resident experience. The platform provides financial management and oversight (including online payments and a secure payment portal), AI-driven reporting and forecasting (Cephai), communication and resident engagement tools, online voting and surveys, maintenance/work-order coordination, compliance tracking, and centralized data and insights. CINC targets professional association management firms and homeowner boards with automation, security, and scalability; the company reports 1,000+ association management company customers, 51,000+ homeowner associations served, 6M+ doors, and $11B+ payments processed annually.

📋 Description

• Design and build robust, scalable, and secure data pipelines for analytics, APIs, and AI applications • Architect and maintain modern data infrastructure across cloud environments, with AWS preferred • 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 for analytics and operational use • Design data contracts and governance patterns supporting data lineage, versioning, and reliability across microservices and AI systems • Build streaming and event-driven data architectures for low-latency, high-integrity data flows • Implement data quality automation and observability systems to detect anomalies and validate pipeline health • Work with Product and Analytics to define KPIs, metrics, and usage data pipelines • Collaborate with AI engineers on embedding pipelines, RAG data sources, and feature stores • Improve data engineering practices through code reviews, pairing, and knowledge sharing • Participate in architecture reviews and contribute to standards for data security, compliance, and scalability • Use AI-native tools and techniques for data classification, anomaly detection, and metadata enrichment

🎯 Requirements

• 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 engineering approach and ability to lead by doing • Excellent communication and ability to bridge engineering, product, and analytics teams • Ability to diagnose system constraints and simplify complex data flows • Collaborative mindset, strong ownership, and focus on measurable business impact • Comfortable mentoring others and setting standards for data craftsmanship across teams