Senior Data Developer

• Visier is building the centralized data and control plane—the infrastructure, pipelines, and governance layer—that powers our internal AI transformation across project delivery, customer success, and internal knowledge. As the Data Developer on this initiative, you will own the technical direction of the entire data layer from the ground up. You will design, build, and scale high-throughput ingestion pipelines, continuous warehouse schemas, and the vector-based context engines that allow AI agents to reliably query and act on live organizational data
• In this role, you will leverage your deep data engineering expertise across relational, columnar, and vector data stores to architect secure, production-grade Data Ops platforms. You will apply advanced pipeline patterns, open table formats, and AI-native data capabilities to transform fragmented enterprise data into trusted context for enterprise AI systems
• Architectural Ownership:
  Define and evolve the foundational data architecture across relational, columnar, document, and vector stores, setting technical standards for storage, schema design, and data flows
• Unified Data Ingestion & Warehouse Design:
  Build scalable pipelines and star schemas to consolidate multi-system enterprise data (Salesforce, Gong, Service Now, Gainsight) across project delivery, knowledge assets, and customer intelligence domains
• RAG Context Engine:
  Own the data layer for the RAG-based knowledge base engine by designing embedding pipelines, chunking strategies, metadata schemas, and index update mechanisms to ensure reliable, context-rich retrieval for AI agents
• Data Transformation & Entity Resolution:
  Build robust transformation and aggregation jobs that clean, deduplicate, and resolve entities across disparate source systems to ensure downstream consumption is highly accurate
• Data Ops & Pipeline Reliability:
  Embed software engineering discipline into data workflows by implementing automated testing, continuous integration/continuous deployment (CI/CD), schema drift detection, and end-to-end observability
• Data Governance & AI Security:
  Integrate role-based access controls, retention policies, and data security standards directly into the architecture to safeguard retrieval layers against AI-specific security risks

Data Modeling & Storage Breadth:
Expertise in dimensional modeling (star schema, normalization) alongside hands-on command of relational, No

SQL, and columnar storage, including open table formats like Apache Iceberg Vector Stores & RAG

Infrastructure: Production experience with vector databases (e.g., pgvector, Pinecone, Weaviate) and building end-to-end embedding pipelines (chunking strategies, indexing, similarity search)
Software Developing Discipline:
Proficient in Python and scripting languages, with strong Git and CI/CD habits (Jenkins, Git Hub Actions) applied to Data Ops practices

Education & Experience:

5+ years of professional data engineering experience with a proven track record of independently owning enterprise data architecture decisions end-to-end; a Bachelor’s degree in CS, Data Science, or related field is preferred AI Security & Integration Awareness:
Practical understanding of how LLM-based agents consume data, alongside knowledge of securing AI‑connected data layers against risks like prompt injection and unauthorized data access

Modern Data Stack Mastery:
Deep hands‑on experience with cloud data warehouses (Snowflake, Databricks), modern ETL/ELT frameworks (dbt, Airflow, Airbyte, Fivetran), and advanced SQL optimization techniques

You never stop learning

You are proud

You make it easy

You roll up your sleeves

You play to win