Senior 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:
  • Zero-copy cloning
  • Resource monitors
  • Streams, Tasks, Pipes
  • 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.