Senior Data Engineer

• 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

• 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

• 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