Analytics Engineer for Narvar focused on building data infrastructure and enabling internal analytics. Collaborating with stakeholders to deliver metrics, models, and self - serve analytics solutions.
About the role
- Data & Analytics Engineer at Prophix building a data platform on Snowflake. Collaborating with finance teams and engineering data architectures for analytics and AI capabilities.
Responsibilities
- Work across the Snowflake platform with real depth: multi-cluster warehouse configuration, resource monitors, query profiling, materialized views, Dynamic Tables, and Snowpark-based compute patterns. You will grow into full ownership of this layer
- Design and optimize schemas using star and snowflake dimensional models; govern clustering keys, search optimization, and micro-partition pruning strategies for large-scale analytical workloads
- Implement and manage Snowflake security architecture: RBAC, row-level and column-level security policies, data masking policies, and network policies
- Build incremental pipelines using Snowflake-native Streams, Tasks, and Dynamic Tables, keeping logic inside the warehouse and removing the need for external scheduling tools
- Drive cost governance through virtual warehouse right-sizing, auto-suspend/resume configuration, result cache optimization, and credit consumption monitoring
- Manage environment lifecycle across dev, staging, and production using zero-copy cloning, time travel, data sharing, and failsafe strategies
- Design and maintain production-grade ELT pipelines from Salesforce (SOQL, Bulk API, CDC), Gong, ChurnZero, Pendo, Eloqua, and Demandbase into Snowflake using Python and AWS-native tooling (Lambda, Glue, S3)
- Build REST API connectors and integration frameworks with retry logic, idempotency, dead-letter queue patterns, and schema drift handling so pipelines do not fall over when source systems change
- Treat data infrastructure like software: automated testing, peer code review, and a clear promotion path from development through staging to production. Nothing goes live without passing those checks.
- Own pipeline monitoring: SLA tracking, alerting, data lineage documentation, and incident resolution with a clear root cause every time
- Build the data foundations that AI runs on: feature stores, embedding pipelines, and clean gold-layer datasets that LLM and agentic workflows can actually use
- Use Snowflake Cortex LLM functions (COMPLETE, SUMMARIZE, SENTIMENT, EXTRACT_ANSWER) to enrich operational data inside the warehouse, so you are not making unnecessary round-trips to external AI APIs
- Build Cortex Search and Cortex Analyst integrations so business users can query Snowflake data in plain English without needing to write SQL
- Build agentic data pipelines using Snowflake Notebooks and Snowpark for things like anomaly detection, automated data preparation, and insight generation
- Identify manual data processes that AI can take over, then build the pipelines and infrastructure to make it happen
- Design conceptual, logical, and physical data models: entity relationship diagrams, dimensional models (star/snowflake schema), and semantic layer definitions aligned to business requirements
- Build and maintain architecture, enforcing data contracts and automated quality checkpoints at each layer
- Implement data quality checks: profiling, validation rules, anomaly detection, and visibility into data health that stakeholders can actually see and act on
- Maintain full data lineage documentation across all sources, transformations, and consumption layers
- Define and enforce data contracts with upstream source system owners: agreed schemas, change notification processes, and SLA expectations that stop pipeline failures before they happen
- Own the Snowflake governance layer: object tagging, data classification, access policy enforcement, and audit logging across all environments
- Manage schema versioning and Snowflake object changes through infrastructure-as-code (Terraform or Snowflake Git integration), so promoting changes across environments is controlled and documented
- Build and maintain a data catalog that gives analysts and stakeholders a clear, trusted view of what data exists, where it comes from, and what it means
- Design Snowflake views, aggregates, and semantic layers with Tableau performance in mind: live connection optimization, extract-friendly structures, and query patterns that do not kill warehouse credits
- Partner with Analytics Engineers on how data models surface in Tableau: what breaks a viz, what slows an extract, and how to structure data so analysts can build without needing engineering support on every dashboard
- Understand the difference between a model that is technically correct and one that is actually usable. Build for the latter.
- Take requirements from RevOps, Finance, CS, and Executive stakeholders and turn them into precise technical specifications. Nothing gets lost in translation.
- Surface data quality issues proactively, before they reach reports or executive decisions.
Requirements
- 4+ years of production data engineering experience in a cloud-native environment
- Deep, hands-on Snowflake expertise: warehouse architecture, performance tuning, RBAC, clustering, micro-partition management, Streams, Tasks, Dynamic Tables, and cost governance
- Hands-on Snowpark experience: writing Python workloads that execute inside Snowflake, including DataFrames, UDFs, and stored procedures. This is how the AI pipelines in this role get built.
- Strong Python proficiency: pipeline scripting, REST API development, AWS Lambda and serverless patterns
- Advanced SQL: complex multi-table transformations, window functions, recursive CTEs, and query execution plan optimization
- Semi-structured data handling in Snowflake: VARIANT type, FLATTEN, LATERAL FLATTEN, and dot-notation querying. Most of our source systems (Salesforce, Gong, Pendo, ChurnZero) deliver nested JSON and you need to be comfortable flattening it
- Git beyond basic version control: branching strategy for data infrastructure, pull request workflows, and working with Snowflake's native Git integration to sync and manage Snowflake objects directly from a repo
- Proven experience integrating Salesforce data via SOQL, Bulk API, or CDC into a cloud data warehouse
- Hands-on experience with AWS-native data tooling: Lambda, Glue, S3, and event-driven pipeline patterns
- Ability to manage data infrastructure changes through a structured development lifecycle: version control, automated testing, peer review, and controlled environment promotion using infrastructure-as-code tooling (Terraform or equivalent)
- Familiarity with data governance concepts: data contracts, object tagging, access policy enforcement, and schema change management
- Tableau or equivalent BI tool knowledge to understand how your data models perform in a reporting layer. You do not need to build dashboards, but you need to know what breaks them
- Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, or equivalent practical experience
- Must be legally entitled to work in Canada or the United States; valid passport required for occasional travel
- Comfortable using AI tools responsibly to support tasks such as research, drafting, and data review
- Able to learn new tools and adapt as technology and workflows evolve.
- Curious, open to new approaches, and motivated to continuously improve.
- Collaborative mindset when working across teams and with AI supported tooling.
Benefits
- Comprehensive health, dental, vision, and mental-health coverage
- Retirement savings with employer contributions
- Parental leave top-up
- Annual wellness allowance
- Generous paid time off including vacation and sick time
- Education assistance and tuition reimbursement
- Social events, team activities, and opportunities to build community
- Opportunities to participate in Environmental, Social, and Governance (ESG) initiatives
- Quarterly Town Halls and Kickoffs that bring teams together to celebrate wins, share updates, and look ahead at what’s next
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