Data & Analytics Engineer

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

We hire for potential as much as experience. If this role excites you but you don’t check every box, we still want to hear from you. At Prophix, people who ask good questions, adapt quickly, and bring a fresh perspective often make the biggest impact.

Required Qualifications

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.

Preferred Qualifications

Hands-on Snowflake Cortex experience: Cortex Search, Cortex Analyst, LLM functions (COMPLETE, SUMMARIZE, EXTRACT_ANSWER), and vector embedding pipelines

Experience designing or operating agentic AI workflows or LLM-integrated data pipelines in a production environment

Advanced Snowflake performance engineering: reading query profiles, diagnosing spill-to-disk, identifying partition pruning issues, and knowing when the problem is warehouse sizing versus query design

Event-driven and near-real-time pipeline architecture: Snowpipe Streaming, Kafka connector for Snowflake, or CDC at the infrastructure level beyond standard API polling

Experience building automated data quality checks into pipelines: row count validation, null rate monitoring, referential integrity testing, and alerting patterns that surface failures before they reach end users

Snowflake Data Sharing and Marketplace: building and consuming secure data shares, reader accounts, or native app listings

Python packaging and dependency management for production environments: virtual environments, packaging standards, and dependency pinning that other engineers can reliably run and maintain

Experience modeling SaaS revenue metrics in a data warehouse: ARR, NRR, churn, logo retention, or pipeline conversion

Familiarity with GTM and RevOps data sources: Gong, ChurnZero, Pendo, Eloqua, or Demandbase

Snowflake certifications (SnowPro Core, SnowPro Advanced: Data Engineer) or AWS certifications (Solutions Architect, Data Analytics Specialty)

Experience in a PE-backed, high-growth SaaS environment

30 days: You have mapped the Snowflake architecture, data sources, and pipeline topology. You have shipped a meaningful improvement to an existing pipeline or integration.

90 days: You own one or more critical data domains end-to-end. Stakeholders trust the data you ship. You are proactively identifying gaps and bringing solutions before being asked.

6 months: You have raised the platform's reliability and AI-readiness. You have delivered at least one Cortex or agentic capability that creates measurable business value. You are a trusted technical partner to Analytics Engineers and senior stakeholders.

Prophix is headquartered in the GTA, so joining our team puts you close to where decisions are made, strategy is set, and careers are built. You’ll collaborate across North America, Europe, and Australia, developing breadth you simply can’t get at a single market company. Our people move across functions, work directly with customers in different industries, and take on meaningful challenges early. We’re a company that competes on talent. That means we invest in the people who show up curious, move fast, and want to leave things better than they found them. Our values are Pursue Excellence, Build with Purpose, Create Wins for All, and Drive Continuous Innovation, and they guide how we actually make decisions.

If you’re looking for a place where your work touches real products, real customers, and real decisions, and where your career can grow in the direction you choose. We would love to meet you. Apply and let’s talk about what’s possible.

Accessibility & AI Transparency

Prophix promotes an accessible hiring process. If you need accommodation at any stage, we’ll work with you. Some interviews may be recorded so our hiring team can review and assess responses fairly and consistently. As part of our commitment to Responsible AI, we use a small number of AI-supported tools to help with tasks like resume review, shortlisting, or creating interview summaries. AI is never used as the sole basis for hiring decisions, and your personal data is never used to train AI models. If you'd prefer not to take part in any AI-assisted step, just let us know and we’ll be happy to accommodate.