Data Engineer

This is a fully remote position, open to applicants in United States.

📋 Description

• Design, maintain, and enhance batch and low-latency data ingestion pipelines sourced from enterprise systems, APIs, and other authorized channels.

• Adhere to the AI Software Development Life Cycle (SDLC) by utilizing approved AI coding tools and agents for generating, refactoring, explaining, and reviewing code; assess generated outputs through engineering judgment, testing, and peer evaluations.

• Employ AI to create and refine unit, integration, data-quality, and regression tests, ensuring that automated tests accurately verify intended functionalities.

• Utilize AI-assisted workflows to develop and maintain technical documentation, data-product documentation, runbooks, lineage notes, and change summaries.

• Strive for coordinated multi-agent delivery patterns encompassing discovery, implementation, testing, documentation, and operational support while ensuring human accountability.

• Create SQL and Python solutions that gather, validate, transform, and distribute data for downstream use.

• Implement reliable transformations, reusable models, curated datasets, and data products utilizing Snowflake and dbt across raw, common, and curated data layers.

• Convert business and technical requirements into source mappings, data models, acceptance criteria, and sustainable engineering solutions.

• Collaborate with analytics, application, AI, Integration Platform, and business teams to ensure governed and documented data access.

• Conduct data quality assessments for completeness, freshness, uniqueness, consistency, referential integrity, and other relevant metrics.

• Incorporate metadata, documentation, lineage, ownership, and usage guidance into data products.

• Establish secure access protocols in collaboration with Data Governance and Security teams, including role-based access, classification tags, masking, and row or column-level controls.

• Develop automated tests and deployment processes across development, quality assurance, and production environments.

• Oversee pipeline health, data freshness, processing efficiency, and failures; troubleshoot issues and engage in incident resolution.

• Optimize Snowflake workloads, queries, transformations, and storage strategies for enhanced performance, reliability, and cost-effectiveness.

• Assist in the curation and dissemination of cross-system data for shared business context, entity-aware access, reporting, automation, and AI applications.

• Collaborate with Integration Platform and semantic-layer functionalities, including Horizon, to ensure consistent business understanding and reusable data access.

• Engage in backlog refinement, estimation, code review, technical documentation, and iterative delivery within an Agile engineering team.

• Discover opportunities to streamline delivery, minimize duplicate tasks, enhance platform standards, and bolster data engineering reliability.

⛳️ Requirements

• Bachelor’s degree in computer science, information systems, engineering, mathematics, or a related discipline, or equivalent experience.

• At least 3 years of experience in data engineering, software engineering, analytics engineering, or a similar technical role.

• Proven experience in writing production-quality SQL and Python code.

• Background in building or supporting data pipelines, transformations, and data models in a cloud data environment.

• Familiarity with Snowflake, dbt, or similar cloud data warehouse and transformation technologies.

• Knowledge of data modeling, ELT/ETL patterns, pipeline orchestration, APIs, and source-system integration.

• Experience with software engineering practices, including source control, code review, automated testing, and CI/CD.

• Active engagement with AI-assisted software development tools for code generation, test creation, documentation, debugging, or review.

• Capability to follow an AI SDLC and spot practical opportunities for collaborative agents to enhance delivery speed, consistency, and coverage.

• Understanding of data quality, metadata, lineage, access control, privacy, and secure management of enterprise data.

• Ability to investigate data issues, clearly communicate findings, and navigate ambiguity with teammates and stakeholders.

• Strong collaboration skills with engineers, analysts, product owners, governance partners, security teams, and business stakeholders.

• Experience with Azure services, serverless functions, cloud storage, or other cloud-native data engineering capabilities.

• Proficient with REST or GraphQL APIs and data ingestion from enterprise applications like Salesforce, Hatch, NetSuite, or similar systems.

• Familiarity with orchestration, event-driven processing, observability, data catalogs, lineage tools, or data quality platforms.

• Experience in supporting semantic models, MCP-based access, or other governed interfaces for analytics, applications, automation, or AI workflows.

• Background in working with master data, reference data, entity resolution, or shared business definitions across multiple systems.

• Experience in managing data products with documented ownership, access expectations, quality measures, and support procedures.

• Familiarity with AI agents or agentic workflows to facilitate software delivery, data engineering, testing, documentation, or platform operations.

• A keen interest in emerging data platform technologies and a practical approach to their adoption.

• Ability to safely and effectively perform essential job functions in accordance with the ADA, FMLA, and other federal, state, and local regulations.

• Capability to maintain regular, punctual attendance in compliance with the ADA, FMLA, and other federal, state, and local standards.

• Primarily office and computer-based work with standard engineering and collaboration expectations for an enterprise technology role.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Generous paid time off and flexible working arrangements.

• Professional development opportunities and continuous learning programs.

• Collaborative and innovative work environment.