AI Snowflake Data Engineer(W2 only)

About the Role

We are looking for a Senior Snowflake Data Engineer with deep expertise in modern data

platforms and large‑scale cloud data architectures. This role is part of a high‑visibility initiative

to build a unified enterprise data foundation powering advanced analytics, AI/ML workloads,

and mission‑critical decision systems.

You will design complex Snowflake architectures, lead data engineering best practices, mentor

engineers, and drive end‑to‑end data platform modernization at scale.

This is a role for senior, hands‑on engineers who excel in solving hard problems, optimizing

systems, and driving technical excellence in fast‑paced environments.

Key Responsibilities

Architecture & System Design

• Own the end‑to‑end architecture, design, and optimization of Snowflake environments.

• Build scalable data ingestion, transformation, and orchestration frameworks capable

of handling high‑volume, high‑velocity enterprise data.

• Architect complex ELT pipelines, using Snowflake Streams, Tasks, Snowpipe,

Materialized Views, and dynamic tables.

• Create performant dimensional and data vault models with strong understanding of

warehouse design principles.

Advanced Engineering & Optimization

• Lead performance tuning, including clustering, micro‑partition optimization, and query

acceleration strategies.

• Drive cost governance, warehouse sizing strategies, auto‑suspend/auto‑resume setups,

and resource monitoring.

• Build reusable frameworks for schema evolution, metadata management, and

automated quality checks.

• Develop CI/CD workflows for data transformations, infrastructure-as-code, and

versioned data pipelines.

AI/ML Data Enablement

• Partner closely with AI/ML teams to deliver feature‑ready datasets, high‑throughput

pipelines, and real‑time data delivery mechanisms.

• Architect data flows to support model training, validation, batch/real-time inference,

and lineage tracking.

• Enable feature stores, embedding pipelines, and vectorized data workflows where

needed.

Leadership & Collaboration

• Provide technical leadership to data engineering teams, drive best practices, and guide

architectural decisions.

• Work with cross‑functional stakeholders—platform engineering, product, analytics, and

security—to build a cohesive data ecosystem.

• Lead code reviews, mentor junior engineers, and raise the overall engineering bar.

Governance, Reliability & Security

• Implement strong role-based access control, data masking, and enterprise‑grade

security frameworks.

• Establish data quality SLAs: validation rules, anomaly detection, automated

reconciliation.

• Build monitoring dashboards for pipeline observability, reliability metrics, and incident

response workflows.

Required Qualifications

• 6–12+ years of experience in data engineering, with deep hands‑on Snowflake

expertise.

• Expert-level proficiency in SQL, advanced query optimization, and distributed data

processing concepts.

• Strong experience with Python and building production-grade data pipelines.

• Hands‑on experience with Airflow, dbt, Dagster, or similar orchestration/ELT tools.

• Strong understanding of cloud ecosystems (AWS/Google Cloud Platform/Azure) including IAM,

networking, object storage, and security.

• Proven track record designing enterprise-scale data architectures for complex analytics

or AI platforms.

• Experience leading engineering efforts, mentoring, and driving technical direction.

Preferred Qualifications

• Experience supporting AI/ML engineering workflows or building ML‑ready data layers.

• Deep knowledge of Snowflake features such as:

o Zero-copy cloning

o Resource monitors

o Streams, Tasks, Pipes

o Time Travel & Fail-safe

• Exposure to event-driven data pipelines, Kafka, Kinesis, Pub/Sub, or similar platforms.

• Background in consulting, platform modernization, or large enterprise transformation

programs.

What Success Looks Like

• You design high‑performance, scalable Snowflake data systems that handle complex

business & AI use cases.

• You proactively identify architectural gaps and deliver robust, forward-looking solutions.

• You mentor engineers and become a technical backbone for the data platform.

• You consistently deliver reliable, high-quality data to downstream AI, analytics, and

operational systems.