Senior Data Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineer based in United States.

The Senior Data Engineer will take end-to-end ownership of a business domain’s data, ensuring ingestion is reliable, accurate, and scalable.

You will design and maintain robust pipelines across APIs, webhooks, files, databases, CDC, and batch sources.

A major focus of the role is building data-quality frameworks that make critical business metrics trustworthy and failures visible early.

You will work closely with data leadership and business stakeholders to transform requirements into well-tested, documented production systems.

The role also involves architecting a clean, traceable single source of truth that can support both analytics and AI-driven applications.

As a senior technical contributor, you will establish engineering standards, improve platform reliability, and mentor other engineers.

This is an opportunity to make a direct impact in a fast-moving, ownership-driven environment where data quality is treated as a core product.

Accountabilities

• Design and build reliable data ingestion pipelines using third-party connectors, APIs, webhooks, file drops, CDC, and batch loads.
• Develop robust API integrations covering authentication, OAuth2 and API-key flows, token refresh, pagination, rate limiting, retries, backoff, and incremental data retrieval.
• Manage schema drift, incremental and full-refresh strategies, idempotency, replayability, backfills, and late or duplicate records.
• Build comprehensive data-quality testing and monitoring, including freshness, volume, schema, referential integrity, uniqueness, source reconciliation, and anomaly detection.
• Ensure data failures are identified quickly through effective validation, alerting, and monitoring mechanisms.
• Own complete domain pipelines spanning ingestion configuration, raw data landing, dbt staging and mart models, quality tests, orchestration, and operational runbooks.
• Translate business definitions into accurate, tested transformations while maintaining authoritative documentation and metric traceability.
• Architect clean, consistently structured, well-documented data that can serve as a reliable single source of truth for analytics, dashboards, AI systems, and agent-driven applications.
• Implement observability, monitoring, and alerting across pipelines and platform dependencies while maintaining required freshness, uptime, and performance standards.
• Optimize pipeline performance, compute utilization, scalability, and overall system efficiency.
• Maintain version-controlled data infrastructure and CI/CD workflows using modern engineering practices.
• Establish reusable reference implementations, particularly for ingestion and data quality, and help raise engineering standards across the team.
• Review other engineers’ work, pair on complex technical challenges, and provide mentorship and practical guidance.
• Collaborate directly with stakeholders to understand business objectives and translate them into reliable, scalable data solutions.

Requirements

• 5+ years of experience building and maintaining production data pipelines.
• Expert-level proficiency with dbt, including staging and mart architecture, incremental models, testing, macros, documentation, and exposures.
• Deep expertise in API-based data ingestion, including authentication, token refresh, OAuth2, API keys, pagination, rate limiting, retry and backoff strategies, and incremental data reconciliation.
• Demonstrated experience working with webhooks, files, databases, and CDC, including schema-drift management, idempotency, backfills, and incremental loading.
• Strong experience designing comprehensive data-quality frameworks that go beyond basic validation to include freshness, volume, reconciliation, anomaly detection, and automated alerting.
• Strong Python skills for production-grade extraction, loading, automation, and data engineering tooling.
• Fluent SQL skills and hands-on experience with a modern cloud data warehouse such as BigQuery or Snowflake, with comparable platforms also considered.
• Experience designing a clean, documented, and AI-ready single source of truth that downstream analytics and AI consumers can use reliably.
• Experience with orchestration technologies such as Airflow, Dagster, or dbt Cloud Jobs.
• Proficiency with Git and collaborative software development workflows.
• Strong problem-solving, communication, and stakeholder-management abilities, with the confidence to work independently and take ownership.
• A proactive, adaptable mindset with a strong commitment to accountability, quality, teamwork, and continuous improvement.
• Ability to work with urgency in a high-expectation environment while maintaining technical rigor and reliability.

Benefits

• Base salary of $148,800-$181,700, commensurate with experience.
• Compensation ranges may vary by geography based on local market rates and cost of labor.
• Medical, dental, and vision insurance.
• 401(k) retirement plan with company match.
• Health Savings Account (HSA).
• Life insurance.
• Disability coverage.
• Paid time off.
• Paid parental leave.
• Fully remote work environment.
• Opportunity to take significant ownership of data architecture, reliability, quality, and AI-readiness.
• Collaborative environment focused on growth, accountability, technical excellence, and meaningful impact.