Staff Data Engineer

The Role & Scope As a Staff Data Engineer, you will initially serve as the sole technical owner of the client's data platform, responsible for stabilizing, optimizing, and scaling its architecture. As the engagement grows, you will build, lead, and mentor a dedicated pod of junior and mid-level engineers.Because you will be embedded deeply with the client, you will interact directly with their executive and business stakeholders (e.g., Finance, Product, and Operations leads) to translate complex business problems into robust data architecture. Tech Stack You Will Own: Storage & Compute: Snowflake (including Snowflake Cortex for LLM agents) Transformation & Modeling: dbt Cloud Ingestion: Fivetran (extracting from microservices, PostgreSQL, Salesforce, QuickBooks, and proprietary systems) Cloud Platform & Infrastructure: AWS (IAM, S3, EC2, PrivateLink) managed via Terraform Downstream Consumption: Tableau, Metabase, and production ML scoring pipelines Key Responsibilities Technical Leadership & Execution Platform Ownership: Act as the sole maintainer and architect of the dbt/Snowflake warehouse, ensuring it remains the company's reliable single source of truth. Operational Engineering: Maintain and harden business-critical operational logic housed within the warehouse (e.g., complex financial commission calculation systems). Infrastructure as Code: Manage, provision, and scale all data infrastructure securely using Terraform. Next-Gen Data Capabilities: Partner with data science teams to support machine-learning pricing/scoring processes and integrate Snowflake Cortex LLM agents. Client Strategy & Stakeholder Management Direct Collaboration: Interface directly with the clients executive leadership and business heads to gather requirements, define metrics, and align the data roadmap with business goals. Defensive Architecture: Translate ambiguous business requests (like changing financial commission structures) into rock-solid, audited data workflows. Team Scaling & Mentorship Pod Leadership: Transition the platform from a solo-operated system to a team-managed environment by onboarding and guiding a small pod of IndexNine engineers. Code Quality: Establish modern DataOps standards, including rigorous code reviews, dbt testing, CI/CD pipelines, and data quality monitoring. Required Technical Qualifications 7+ years of total experience in Data Engineering, Software Engineering, or Cloud Architecture. Deep Snowflake Expertise: Minimum 3+ years of advanced hands-on experience in architectural tuning, performance optimization, and access control in Snowflake. Expert-Level dbt: Strong mastery of dbt (ideally dbt Cloud), including advanced macro development, incremental modeling strategies, and custom test suites. Production Cloud & IaC: Proven experience managing cloud data infrastructure with Terraform, specifically in the AWS ecosystem (including VPCs, Lambda, Glue, Athena, IAM roles, security groups, and S3 event routing). Pipeline Orchestration: Deep understanding of automated ingestion tools (Fivetran, HighTouch) and microservice database architectures (PostgreSQL). Software Engineering Mindset: Strong SQL skills are a given; you must also possess robust programming fundamentals (Python preferred) to interface with ML models and API endpoints. Preferred Qualifications (The Extra Edge) Certifications: Snowflake Certified Core Professional or Advanced Data Engineer and/or dbt Analytics Engineer and/or AWS Certified Data Engineer. AI/ML Exposure: Hands-on familiarity with LLM integrations, vector databases, or embedding frameworks (specifically Snowflake Cortex or similar cloud-native AI tools). Consulting DNA: Prior experience in a client-facing technology consulting or services delivery role, with a proven track record of managing executive client relationships. FinTech/Logistics Domain Knowledge: Experience building data platforms that touch accounting/ERP systems (QuickBooks, Salesforce) or complex billing/commission infrastructure.