Data & Analytics Engineer

Prophix Snowflake Platform Engineering Role

At Prophix, we're building the platform that helps finance teams stop managing spreadsheets and start driving strategy. Prophix One™ brings planning, reporting, consolidation, and automation together in a single place, and we are expanding its AI capabilities faster than ever. If you want to work on a product that genuinely changes how finance teams operate, and do it alongside people who care about getting it right, this is where you want to be.

Prophix is building a data platform on Snowflake that drives revenue, customer success, finance, and executive decision-making. Data infrastructure, AI, and business strategy are all moving at the same time here, and this role sits at the center of that. It is not a steady-state job.

You will own the pipelines and integrations that move data from Salesforce, Gong, ChurnZero, Pendo, Eloqua, and Demandbase into Snowflake, and the platform architecture that keeps that data clean, fast, and ready for AI. You take ownership of outcomes, not just tasks, and you build for the next engineer as much as yourself. You work directly with the Director of Revenue Operations & Analytics and alongside Analytics Engineers, and senior business stakeholders, and you can make sense to all of them.

If you know Snowflake well, think in systems, and want to build data infrastructure that shows up in board-level reporting and agentic workflows, this role is worth a conversation.

Snowflake Platform Engineering

• 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

Pipeline & Integration Engineering

• 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

AI-Enabled Data Engineering

• 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

Data Modeling & Quality

• 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

Data Governance

• 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

BI & Reporting Layer

• 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.

Stakeholder Partnership

• 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