Data Engineer - Snowflakes/AWS

Snowflake Developer / Data Engineer designs, builds, optimizes, and maintains large-scale cloud data platforms and ETL/ELT pipelines using Snowflake.

Core Responsibilities

• Data Pipeline & Architecture: Design, build, and deploy scalable data ingestion and transformation workflows.
• SQL & Performance Optimization: Write, tune, and troubleshoot complex SQL queries, stored procedures, views, and Snowflake virtual warehouses for speed and cost efficiency.
• Data Modeling: Design dimensional models, star/snowflake schemas, fact/dimension tables, and Slowly Changing Dimensions (SCD).
• DevOps & CI/CD: Implement version control (Git/GitHub), CI/CD pipelines, state-based deployments, and dbt (data build tool) integrations.
• Security & Governance: Ensure role-based access control (RBAC), data masking, encryption, and compliance best practices are applied.

• Python: Backend service development, REST API integration, retry and error-handling mechanisms, data schema modelling and validation with Pydantic
  SQL and Snowflake: MERGE- and stream-based CDC pipelines, Tasks, stored procedures, Secure Views, Snowpipe and Snowpipe Streaming, Secure Data Sharing, RBAC, role and grant management, and warehouse performance tuning
  Terraform: Infrastructure as code for Snowflake databases, roles, grants, warehouses, and repeatable multi-environment deployments
  
  
• AWS: Lambda-based event-driven ingestion, Amazon S3 data lake storage, AWS Glue Data Catalog, and IAM-based access controls
  Palantir Foundry: Workshop-based GUI development and backend workflow orchestration for data-request, approval, and provisioning processes
  Version Control and CI Engineering: Git-based development workflows, pre-commit hooks, Ruff-based linting and formatting, pull-request templates, review gates, and structured AI-assisted development workflows
  Data Modelling and Interchange Formats: JSON-based configuration and request schemas, and synchronisation of hierarchical and relational data models across systems
• Platform and Domain Expertise
  Enterprise data provisioning: End-to-end ownership of data-request portals using Palantir Foundry Workshop, Foundry workflow orchestration, and Python backend services targeting Snowflake and AWS
  Data virtualisation and analytics integration: Output-port configuration, ownership and privilege logic, and hierarchical data-model synchronisation between source systems and downstream platforms
  Industrial and OT data integration: Event-driven ingestion of Aveva PI process-historian data using Lambda, Snowpipe, and Snowflake, with downstream delivery through Secure Data Shares
  Multi-stage approval workflows: Role-based approval lifecycles involving requesters, data stewards, data owners, engineering teams, and business verifiers
  Data access governance: Request and approval workflows, role-based access control, and self-service provisioning for business data owners
  Regulated-environment delivery: Dev-to-QA-to-production promotion workflows supporting validated and regulated, GxP-equivalent system requirements

Required Skills & Qualifications

• Experience: Typically 3 to 5+ years of hands-on experience in data engineering or cloud data warehousing.
• Core Technologies:
• Snowflake Features: Virtual warehouses, clustering, micro-partitions, Time Travel, Streams & Tasks, Snowpipe, Dynamic Tables, and Snowflake Cortex (where applicable).
• Programming & Scripting: Expert-level SQL and strong Python (Pandas, NumPy).
• Transformation Tools: dbt (models, snapshots, tests, macros, Slim CI).
• Cloud & Orchestration: Exposure to AWS, Azure, or GCP, plus tools like Apache Airflow.
• Certifications: Preferred credentials often include SnowPro Core or advanced Snowflake certifications.