Data Engineer Snowflakes AWS

Job description

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.