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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Engineer - **Company:** Fictiv Inc. - **Location:** Schaumburg, IL, United States - **Experience:** Expert - **Salary:** $167,000.0 - $202,300.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Audit Trail, Automation of Tests, Big Data, Cloud Computing, Cluster Analysis, Code Review, Information Systems, Continuous Integration, Customer Data Management, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Sharing, Data Warehousing, Software Debugging, Dimensional Modeling, Data Flow Control, Github, JSON, Netsuite, Performance Tuning, Query Optimization, Salesforce.Com, SQL Databases, Tableau (Software), Talend, Data Classification, Macros, Sql Optimization, Snowflake, AI Coding Agents, Agentic-AI, Git, Semi-structured Data, Information Technology, Performance Monitor, Codebase, Data Management, Terraform, Looker Analytics, Data Pipelines - **Published:** October 22, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=684ac1c44b409dac ## About the Role Five core skills. Candidates must show direct, hands-on production experience in each: * Snowflake administration and engineering: access control, warehouse sizing and cost control, query tuning, clustering, security controls, cloning, time travel, and data sharing. * dbt expert modeling: layered models, dimensional modeling, incremental models, snapshots, macros, tests, documentation, and CI/CD through Git. * Advanced SQL: CTEs, window functions, recursive queries, semi-structured data, and query tuning. * ETL/ELT tooling: Fivetran (our tool) or similar, including connector setup, schema drift, deleted records, backfills, and cost. * Agentic coding and development: daily use of AI coding agents that work in the repository and terminal (not chat-only use), with disciplined verification of everything they produce., * Bachelor's degree in Data Science, Computer Science, Information Systems, or a related field, or equivalent practical experience. * 10+ years of experience as a data engineer, designing, building, and operating data pipelines and warehouses. * 6+ years of hands-on Snowflake experience in production, including administration: access control, warehouse management, and cost and performance monitoring. * 5+ years building and maintaining production dbt projects (dbt Core or dbt Cloud), including testing, documentation, incremental models, macros, and CI/CD. * 5+ years working with ETL/ELT tools such as Fivetran, Airbyte, Stitch, Matillion, Informatica, or Talend, plus experience with pipeline orchestration. * Demonstrated, hands-on experience using agentic coding tools that work directly in the codebase (not chat-only AI use) in daily data engineering work. This includes building agentic workflows or tool integrations for data work, such as MCP servers, custom agents, or automated dbt and pipeline checks, with concrete examples of the workflows and results. * Expert SQL, including CTEs, window functions, and performance tuning. * Daily use of Git and GitHub pull-request workflows. * Experience being the primary or sole owner of a data platform, or the technical lead for one. * Experience administering data platforms that hold regulated data (such as CUI under NIST SP 800-171), including access control, audit logging, encryption, and data classification. Must be able to pass a background check and complete required CUI and security training., * Direct Fivetran experience, including connector management. * Experience with Sigma Computing, or with Tableau, Looker, or a similar BI tool. * Experience modeling ERP and CRM data, such as NetSuite or Salesforce. * Python for data tooling and automation. * Snowflake SnowPro certification or dbt certification. * Infrastructure-as-code experience for Snowflake, such as Terraform. * Background in manufacturing, supply chain, or marketplace businesses., Ability to travel 5% annually in the US for team meetings, offsites, and conferences. ## Description You will work hands-on with a modern stack (Snowflake, dbt, Fivetran, Git/GitHub) and with AI coding agents in your daily engineering work. You will also have the autonomy to take requests from intake to production, propose what we should build next, and shape safe team practices for agentic development. If you want a role where your judgment directly determines whether the business can trust its data, this is it., * Administer Snowflake: manage users, roles, grants, warehouses, resource monitors and security policies, and keep the environment secure, fast and cost-efficient. * Design, build, and own dbt models (staging, intermediate, marts, and the semantic layer) that turn replicated source data into trusted, analytics-ready tables with tests and documentation. * Build, operate, and monitor ELT pipelines from production and business systems into Snowflake, using Fivetran and related tools. Resolve connector failures, schema changes, backfills, and data-quality issues. * Act as the team's primary technical resource for modeling and pipeline work. Take requests from intake to production independently, including scoping, estimating, and reporting status. * Translate business requirements into technical requirements and data models, and bridge the gap between business teams and technology. Ask clarifying questions, agree on metric definitions and acceptance criteria with stakeholders, and turn them into specifications the team can build and test. * Proactively identify opportunities where data, modeling, or automation can help the business get closer to its goals, and propose and prioritize them with stakeholders instead of waiting for requests. * Run an impact analysis before changing, deprecating, or removing any shared table, model, or connector. Use dbt lineage, Snowflake dependency and access metadata, and stakeholder review, and document a rollback plan. * Own the Git and GitHub workflow for the dbt project: branching standards, pull-request review, automated tests, and deployment. * Use AI assistants and agentic workflows in daily engineering work to speed up SQL and dbt development, testing, documentation, code review, and debugging, and help set safe team practices for using them. * Find the root cause of data-quality, performance, and pipeline problems and fix them at the source. * Tune queries, models, and warehouse usage, and advise engineers and analysts on efficient query patterns and table design. * Maintain data dictionaries, data-flow diagrams, and schema documentation. * Lead data design sessions and technical reviews, and explain design trade-offs to technical and non-technical stakeholders. * Evaluate new Snowflake and dbt features and tools, and recommend improvements to the data platform. * Mentor analysts and engineers on SQL and dbt best practices. * Work with remote, cross-functional teams across time zones. * Maintain and contribute to the Quality Management System (QMS)., * Write advanced SQL on large, complex datasets, including semi-structured data such as JSON in Snowflake VARIANT columns. * Apply data warehousing and dimensional modeling principles to turn business requirements into well-structured, maintainable models. * Work independently: take an open-ended problem, define the approach, deliver it, and raise risks early instead of stalling. * Use sound production judgment: verify before changing shared systems, plan for failure, and know how to roll back. * Use AI tools with discipline: give clear context and constraints, review and test every AI-generated change, and stay accountable for the result. * Solve hard problems methodically: break an unclear problem into testable hypotheses, gather evidence from logs, lineage, and query history, confirm the cause before changing anything, and verify the fix worked. * Communicate clearly in writing and in person, giving business context to engineers and technical context to non-engineers.