Lead Data Architect (6-month term)

About Introhive

Introhive is an AI-powered Relationship Intelligence platform that helps firms overcome data silos and unlock actionable relationship insights that drive collaboration and growth.

We've grown a lot since we began our journey in 2012, but our core mission remains the same - To revolutionize the way companies manage, nurture, and leverage their relationships to unlock value, drive growth, and delight customers.

Introhive is the fastest growing B2B relationship intelligence platform, recognized as a category leader in sales intelligence and data quality management software by G2 Crowd, a top 10 fastest growing technology company in Deloitte's Fast 50 Awards three years in a row, and the MarTech 2020 Breakthrough Award winner for Best CRM Innovation.

Trusted by industry-leading brands such as KPMG, Freshfields, CBRE, and Deloitte, Introhive supports over 250,000 users in 90+ countries.

Contract Scope of Work

The need:
Introhive's profile as a B2B SaaS organization centered around data services and we need to modernize the data platform for AI capabilities, reimagine Introhive's data architecture to support not just current needs but future AI capabilities including assistants, agents, and autonomous AI actors - creating a data platform that enables both structured analytics and the more fluid, context-aware needs of modern AI systems.

What you bring in:
As a Lead Data Architect, you will be a vital member of our dynamic team, helping lead the modernization of our core data platform and establish a high-performing, scalable data architecture. The ideal candidate will have an extensive background and expertise in the data domain, including hands-on experience with PostgreSQL, Snowflake, and with experience in building AI and LLM platforms. You will be instrumental in the design and implementation of modern data solutions across a variety of cloud-based architectures.

This role is critical in shaping our data strategy, enabling data-driven decision-making, and ensuring our data platform is built for scalability, security, and efficiency . You will work closely with other engineering and product teams to define the technical foundation for success.

SUPERVISORY DUTIES
You will be responsible for working with a team of professional developers, ensuring a quality solution delivery across your areas of ownership.

RESPONSIBILITIES

• Lead the modernization of our core data platform, ensuring scalability, reliability, and performance.
• Architect and optimize data storage, processing, and retrieval strategies using PostgreSQL and Snowflake .
• Design and develop robust APIs and messagebus for seamless data integration across applications, analytics platforms and data-based services.
• Collaborate within and across engineering, intelligence and web experiences teams to ensure product capabilities and enable the next-generation of the Introhive platform.
• Work with other leaders within the Data pillar to define and enforce data governance, security, and best practices across the organization.
• Evaluate, recommend, and implement modern data architectures and cloud-based solutions .
• Develop ETL/ELT pipelines for structured and unstructured data.
• Support AI & LLM initiatives by ensuring the data platform effectively supports AI-driven applications .

REQUIRED SKILLS & ABILITIES

Technical Expertise

• Advanced Data Architecture Experience: 8+ years designing and implementing large-scale data platforms, with specific experience in real-time data streaming architectures (Kafka, Kinesis, Pulsar)
• Modern Data Stack Proficiency: Deep expertise with cloud-native data technologies and patterns for both batch and streaming workloads
• AI/ML Infrastructure Knowledge: Experience designing data architectures that support advanced AI/ML workloads, including vector databases and embedding models
• Real-time Processing Systems: Proven track record implementing event-driven architectures that support both transactional and analytical workloads
• Data Mesh/Data Fabric Understanding: Experience implementing domain-oriented, decentralized data architectures

Specific Technical Skills

• Streaming Technologies: Expert-level knowledge of Kafka/Confluent ecosystem, Kinesis, or similar streaming platforms
• Vector Databases: Experience with vector databases (Pinecone, Weaviate, Milvus) for AI embedding storage and retrieval
• Cloud Platforms: Deep expertise in AWS (preferred)/Azure/GCP data services, especially managed streaming offerings
• Data Orchestration: Experience with modern workflow orchestration tools (Airflow, Dagster, Prefect)
• Data Transformation: Knowledge of real-time and batch transformation frameworks (dbt, Spark Structured Streaming, Flink)
• Semantic Layer Solutions: Experience implementing metrics layers and semantic models

Strategic Vision

• Data as a Product Mindset: Experience treating data as a product with clear interfaces, SLAs, and ownership
• Modern Data Architecture Patterns: Deep understanding of data mesh, lakehouse, and other modern architectural patterns
• Technical Roadmapping: Ability to create multi-year technical roadmaps for progressive data platform evolution

Soft Skills and Leadership

• Cross-functional Collaboration: Proven ability to work with product, engineering, and business stakeholders to translate business needs into technical architecture
• Architecture Governance: Experience establishing and overseeing architecture review processes
• Technical Mentorship: Ability to mentor and grow data engineering talent within the organization

ADDITIONAL BENEFICIAL SKILLS

AI Readiness Requirements

• LLM Integration Experience: Experience building architectures that support Large Language Model integration
• RAG System Design: Knowledge of Retrieval-Augmented Generation patterns and the data infrastructure required
• Observability for AI: Experience with monitoring and observability specifically for AI/ML systems
• Feedback Loop Design: Knowledge of creating systems that can capture and incorporate user feedback for AI model improvement
• Data Quality for AI: Experience implementing validation frameworks ensuring data quality sufficient for AI consumption

EDUCATION & EXPERIENCE

• University Degree or equivalent in Computer Science or related field required.

• 8+ years experience working with software engineering required.
• 5+ years experience working with data-based solutions required.