Own end-to-end target-state Snowflake architecture for enterprise migration and modernization engagements, including landing zone, RBAC, data sharing, and multi-account design.
Lead current-state assessments of legacy Teradata, Oracle, Netezza, SQL Server, and Hadoop estates; build workload heat maps and migration wave plans.
Define migration patterns (lift-and-shift, re-platform, re-engineer) and decide the right approach per workload based on TCO, risk, and business value.
Architect ELT pipelines on Snowflake using dbt or comparable transformation tooling — establish project structure, model layering (staging, intermediate, marts), testing, documentation, and CI/CD.
Design ingestion and transformation frameworks using Snowpark, Streams & Tasks, Dynamic Tables, and Snowpipe for batch, micro-batch, and streaming sources.
Provide technical leadership to delivery pods of 6 to 20 engineers across data engineering, SQL conversion, ELT modeling, testing, and DevOps tracks.
Drive automation of SQL conversion, data validation, and reconciliation using Snowflake-native tooling and accelerators such as SnowConvert, Bladebridge, or Datametica.
Conduct architecture and code reviews; enforce engineering standards, naming conventions, and reusable patterns across ELT projects.
Own production cutover planning, rollback strategy, and hypercare — ensuring zero data loss and minimal business disruption.
Support pre-sales — lead solution workshops, RFP responses, estimations, and SOW construction for qualified Snowflake opportunities.
Must-Have Qualifications
Experience: 8 to 12 years in data warehousing, data engineering, or analytics platform architecture, with at least 4 years hands-on Snowflake delivery.
Migration depth: Led at least 2 end-to-end migrations from a legacy MPP/EDW (Teradata, Netezza, Exadata, SQL Server, or Hadoop) to Snowflake at enterprise scale.
Snowflake mastery: Strong command of virtual warehouses, RBAC, resource monitors, micro-partitioning, clustering, query profile analysis, time travel, zero-copy clone, secure data sharing, and replication/failover.
Data engineering: Expert SQL plus working proficiency in Python; production experience with Snowpark, Airflow, and at least one cloud-native ingestion tool (Fivetran, Matillion, Informatica IDMC, ADF, AWS Glue).
Cloud fluency: Hands-on architecture experience on at least one of AWS, Azure, or GCP — including IAM, networking (PrivateLink), storage, and key management interplay with Snowflake.
Governance & security: Deep familiarity with data governance constructs — masking policies, row access policies, tagging, object dependencies, and integration with Collibra, Alation, or Atlan.
FinOps: Demonstrated ability to design and operate cost-optimized Snowflake deployments — warehouse sizing strategy, workload isolation, query tuning, and chargeback models.
Consulting craft: Strong written and verbal communication; comfort engaging with senior client stakeholders; experience writing SOWs, estimations, and architecture documents to publication quality.
Hands-on experience with dbt (dbt Core or dbt Cloud) on Snowflake — model layering, macros, tests, snapshots, and CI/CD; or equivalent experience with similar ELT tooling such as Coalesce, Matillion, or Informatica IDMC.
Experience with Snowflake Cortex (LLM functions, Cortex Search, Cortex Analyst) for GenAI use cases.
Hands-on with Apache Iceberg or open-table-format interoperability patterns.
Exposure to streaming architectures using Kafka, Kinesis, Snowpipe Streaming, or Dynamic Tables.
Industry depth in one or more of Banking & Financial Services, Healthcare & Life Sciences, Retail & CPG, or Manufacturing.
Prior experience working in a Snowflake Premier or Elite Partner organization.
