Data and Analytics Engineer

Role Overview

• 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.

Tech Stack

• AWS
• Cloud
• Python
• SQL
• Tableau
• Terraform

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