Senior Data Developer

Visier; Vancouver; On-site; CAD 120,000 - 160,000; Full time

Job summary

Visier is building the centralized data and control plane powering our AI transformation. As the Data Developer, you will own the technical direction of the data layer, designing and scaling ingestion pipelines, warehouse schemas, and RAG embedding engines for reliable AI access to live organizational data.

You will apply data engineering expertise across relational, columnar, and vector stores, architect secure DataOps platforms, and ensure governance and security measures while collaborating

Qualifications

• 5+ years of professional data engineering experience.
• Bachelor’s degree in CS or data-related field preferred.
• Experience with cloud data warehouses and modern ETL/ELT frameworks.

Responsibilities

• Own the technical direction of the data layer from the ground up.
• Design, build, and scale ingestion pipelines and warehouse schemas.
• Develop RAG context engines and embedding pipelines for AI access to data.
• Implement DataOps practices including CI/CD, testing, and observability.
• Ensure data governance and security across data platforms.

Skills

Python

Git

CI/CD

SQL

Data modeling

Education

Bachelor’s degree in CS or data science

Tools

Snowflake

Databricks

Airflow

dbt

Airbyte

Fivetran

Apache Iceberg

pgvector

Pinecone

Weaviate

Job description

• 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 DataOps 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, ServiceNow, 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
• DataOps & 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, NoSQL, and columnar storage, including open table formats like Apache IcebergVector 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, GitHub Actions) applied to DataOps practicesEducation & 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 preferredAI 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 accessModern 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 techniquesYou never stop learningYou are proudYou make it easyYou roll up your sleevesYou play to win