Sr. Snowflake Data Engineer

Lead end-to-end design, build, and delivery of enterprise data platforms, lakehouse architectures, cloud data warehouses, and analytics solutions for regulated industries (banking/financial services preferred). You'll own the solution architecture, guide a team of engineers, and be the technical authority on client engagements.

Responsibilities:

• Architect and deliver enterprise lakehouse and data warehouse solutions on AWS (S3 Glue, EMR, Redshift) with Snowflake as the core analytical engine.
• Design multi-zone data lake architectures (Bronze / Silver / Gold) using open table formats (Delta Lake, Apache Iceberg) with ACID compliance and schema evolution.
• Build and optimise data pipelines batch, CDC, and real-time streaming using tools like AWS DMS, Glue Spark, MSK (Kafka), dbt, and Step Functions/Airflow.
• Define data models: Data Vault 2.0 for integration layers, dimensional star/snowflake schemas for consumption, and semantic layers for enterprise BI.
• Implement data governance frameworks, cataloguing, lineage, data quality (Great Expectations or equivalent), MDM, and BCBS 239 / regulatory compliance controls.
• Lead solution design workshops, produce architecture documents, and present to C-level and steering committees.
• Mentor and technically guide a team of 4-6 data engineers across delivery phases.
• Manage stakeholder expectations, delivery timelines, and quality gates on engagements sized at 6-12+ months.

Requirements:

• 7-10 years in data engineering/data architecture roles, with at least 4 years on Snowflake (Enterprise or Business Critical edition).
• Strong hands-on experience with AWS data services: S3 Glue, EMR, Redshift, DMS, Lambda, Step Functions, IAM, KMS.
• Proven delivery of at least 2 large-scale data platform implementations (50+ users, 10+ source integrations, TB-scale).
• Deep expertise in SQL, Python/PySpark, and data modelling (Data Vault, star schema, bi-temporal).
• Experience with ETL/ELT orchestration tools: dbt, Airflow (MWAA), or equivalent.
• Working knowledge of data governance, data quality frameworks, and metadata management.
• Ability to produce solution architecture documents, cost estimates, and technical proposals.
• Excellent communication comfortable leading client-facing discussions and presenting to senior leadership.

Good to Have:

• Experience in banking, financial services, or other regulated industries (RBI/RMA compliance, PCI DSS, ISO 27001).
• Exposure to AI/ML platforms SageMaker, MLOps, Feature Stores, or GenAI (Bedrock/RAG).
• Snowflake certifications (SnowPro Advanced: Architect or Data Engineer).
• AWS Solutions Architect or Data Analytics Speciality certification.
• Experience with Oracle Analytics Cloud, Power BI, or Tableau for enterprise BI delivery.
• Familiarity with DevSecOps, CI/CD for data pipelines, and infrastructure-as-code (Terraform/CloudFormation).
• Prior experience with historical data migration and source-to-target reconciliation at scale.