Description

Key Responsibilities

1. Ingestion and orchestration

 Build and maintain ingestion into Bronze using Snowflake Openflow, Fivetran, Snowpipe /

Snowpipe Streaming, and stage-based file loads (CSV, email, SharePoint).

 Implement incremental patterns with Streams, Tasks, Dynamic Tables and stored

procedures where they fit better than dbt.

 Monitor connector health, sync cost (MAR and credits), schema drift and late-arriving data.

Add alerting and runbooks.

2. Transformation with dbt

 Develop and maintain dbt projects (models, snapshots for SCD, tests, macros, seeds, docs)

deployed as dbt Projects on Snowflake.

 Model Silver (conformed, cleansed) and Gold (business-ready facts, dimensions, KPI and

SLA measures) layers against a canonical data model.

 Apply data-quality testing, including dbt tests and Snowflake Data Metric Functions. Define

freshness SLAs and handle soft deletes, deduplication and grain issues.

 Schedule and orchestrate dbt runs through Snowflake Tasks or equivalent, with failure

notification.

3. Cortex semantic layer, agents and applications

 Design and curate semantic views and semantic models for Cortex Analyst, including verified

queries, synonyms and relationships.

 Configure and evaluate Cortex Agents (Analyst, Search and custom tools). Establish

question banks and accuracy scorecards, and monitor and control Cortex credit

consumption.

 Build Streamlit in Snowflake applications for business users (reporting, data entry,

operational tools).

 Apply Cortex Search, AISQL functions and Cortex Code-assisted development where they

add measurable value.

4. Governance, security and platform engineering

 Implement role-based access (database, access and functional roles) and tag-based

masking and row access policies for PII such as SSN, bank and compensation data.

 Maintain separate Dev and Prod environments and promote changes through Git-based

CI/CD (GitHub or Azure DevOps), using Git integration, CREATE OR ALTER, and Terraform

or DCM where applicable.

 Apply cost and performance best practices: warehouse right-sizing, resource monitors, query

tuning.

 Support Snowflake security hardening, including MFA, key-pair or OAuth authentication for

service accounts.

5. Technical leadership and collaboration

 Translate business requirements (SLAs, KPIs, reports) from stakeholders into Gold-layer

models and AI use cases.

 Lead design reviews and code reviews, mentor junior engineers, and contribute to estimates,

sprint planning and status reporting.

 Document data models, lineage and operating procedures. Work cooperatively with other

vendors and client teams on the shared platform.

Required Qualifications

 6 to 8 years in data engineering, including 3+ years of hands-on Snowflake experience on

Enterprise edition features.

 Strong SQL (window functions, MERGE, semi-structured data, performance tuning) and solid

Python.

 Production experience with dbt (models, incremental, snapshots, tests, macros, packages).

 Experience building ELT pipelines with Fivetran or similar tools and with Snowflake-native

ingestion (Snowpipe, Streams, Tasks).

 Proven delivery of at least one AI or GenAI solution on Snowflake (Cortex Analyst, Cortex

Agents, Cortex Search or equivalent), beyond a proof-of-concept demo.

 Strong grounding in dimensional modeling, medallion architecture and SCD Type 1 and 2.

 Working knowledge of Snowflake RBAC, masking policies, tagging and data governance.

 Git-based workflow and CI/CD experience.

 Excellent written and verbal communication, with the ability to work directly with client

business users.

Preferred Qualifications

 Hands-on with Snowflake Openflow (NiFi-based connectors) or comparable CDC and

streaming ingestion.

 Experience with dbt Projects on Snowflake, Dynamic Tables and Data Metric Functions.

 Streamlit in Snowflake applications in production.

 Familiarity with Power BI semantic models and DirectQuery or Import trade-offs against

Snowflake.

 Background in staffing, VMS (for example Workday VMS), ATS (Bullhorn) or ERP (Dynamics

GP) data.

 Terraform, Azure DevOps or GitHub Actions experience, and Snowflake cost-governance

practices.

 SnowPro Core and SnowPro Advanced: Data Engineer or Data Scientist certification.

What Success Looks Like (first 6 to 12 months)

 Bronze ingestion for priority sources is automated, monitored and cost-controlled.

 Silver and Gold models are delivered through dbt with passing tests, a defined refresh

schedule and failure alerts.

 Priority KPI and SLA measures are served from governed Gold tables rather than ad hoc

views.

 A Cortex semantic model and agent answer defined business questions at an agreed

accuracy threshold, with usage and cost visible.

 PII is protected through tag-based masking, and Dev-to-Prod promotion runs through version

control.

Competencies

End-to-end ownership of pipelines. Clear written communication and documentation. Comfort with

ambiguous requirements and client-facing work. A habit of verifying data against the live source

before building on assumptions. Curiosity about new Snowflake releases, balanced with judgment

about what is ready for production

Key Skills

Education

Any Gradute