Data Engineer,Sr. - WFH

## About the Role

Gigster is seeking a **Senior Data Engineer, Operations** to support the health, reliability, and correctness of production data pipelines. This hands-on role sits within the Data Platform Operations function and focuses on pipeline triage, debugging, data quality, incident resolution, and operational automation.

You’ll work across data ingestion, orchestration, dbt transformations, and medallion-layer data models while partnering with Data Platform, Analytics Engineering, DevOps, and downstream data consumers. This role is well suited to an experienced data engineer or analytics engineer who enjoys solving complex production issues and improving the reliability and efficiency of data platforms.

## Key Responsibilities

• - Build and maintain automation, scripts, and lightweight tooling for pipeline triage, data validation, backfills, reprocessing, and data quality checks.
• - Improve self-service capabilities and reduce manual operational work.
• - Own operational support for ingestion and transformation pipelines using technologies such as Airflow, Spark, dbt, Kafka, and Snowflake or similar platforms.
• - Triage failed jobs, diagnose data issues, perform backfills, and coordinate fixes across ingestion, transformation, and analytics layers.
• - Monitor pipeline health, data freshness, and data quality metrics across medallion architecture layers.
• - Investigate data anomalies, schema drift, and transformation failures.
• - Perform root-cause analysis and implement corrective actions to resolve data incidents.
• - Serve as a primary interface between Data Platform, Analytics Engineering, and downstream consumers during operational issues.
• - Communicate business and technical impact, coordinate fixes, and drive timely incident resolution.
• - Support schema evolution, data contracts, and downstream data consumers in production environments.

## Required Qualifications

• - **7+ years of experience** in data engineering, analytics engineering, or software development, including significant experience operating and supporting production data pipelines.
• - Must live in the **contiguous United States**.
• - Must have all necessary documentation to work under an **independent contractor agreement**.
• - Strong programming skills in **Python and SQL** on at least one major data platform, such as Snowflake, BigQuery, Redshift, or similar.
• - Experience supporting **schema evolution, data contracts, and downstream consumers** in production environments.
• - Strong experience triaging, debugging, and maintaining **dbt models**, including dependencies across bronze, silver, and gold medallion layers.
• - Experience with **streaming, distributed compute, or S3-based table formats**, including Spark, Kafka, Iceberg, Delta, or Hudi.
• - Experience with **schema governance, metadata systems, and data quality frameworks**.
• - Hands-on experience operating and debugging orchestration workflows such as **Airflow, Dagster, or Prefect**, including retries, backfills, and dependency management.
• - Solid understanding of **CI/CD and Docker**.
• - At least **2 years of experience with AWS**.
• - Ability to work under a full-time independent contractor arrangement during **US Eastern Time office hours**.

## Preferred Qualifications

• - Experience participating in **on-call rotations, incident response, or data operations teams**.
• - Experience with **data observability, data catalog, or metadata management tools**.
• - Experience working with **healthcare data**, including X12 or FHIR.
• - Understanding of authentication and authorization technologies such as **OAuth2, JWT, or SSO**.

## Skills & Competencies

• - Production data pipeline operations
• - Data engineering and analytics engineering
• - Python and SQL
• - Data pipeline troubleshooting and debugging
• - dbt and medallion architecture
• - Airflow and workflow orchestration
• - Data ingestion and transformation
• - Data quality and validation
• - Schema evolution and data contracts
• - Root-cause analysis and incident resolution
• - Streaming and distributed data processing
• - AWS
• - CI/CD and Docker
• - Data governance and metadata management
• - Cross-functional communication
• - Operational problem-solving
• - Automation and reduction of manual operational toil

## Education & Experience

**Education**

- No specific education or degree requirement is stated in the source job description.

**Experience**

• - **7+ years** in data engineering, analytics engineering, or software development, with significant production data pipeline operations experience.
• - **2+ years of AWS experience**.
• - Additional experience requirements are detailed under Required Qualifications.

## Work Arrangement & Schedule

• - **Location:** United States; must reside in the contiguous United States.
• - **Work arrangement:** Fully remote.
• - **Employment type:** Full-time, independent contractor agreement.
• - **Expected weekly hours:** 40 hours/week.
• - **Working hours:** US Eastern Time office hours.
• - **On-call/weekends:** On-call experience is preferred, but the source does not state that participation in an on-call rotation or weekend work is required.
• - **Sponsorship:** Sponsorship and sponsorship transfers are not available. H1B, OPT, EAD, and CPT visas are not considered.

## Compensation & Benefits

• - **Compensation:** Not specified in the source job description.
• - **Contract:** Stable, long-term independent contract agreement.
• - **Remote work:** Fully remote within the contiguous United States.

## Compliance / Additional Information

• - **Employer:** Gigster
• - The role is described as a fast-paced, high-pressure position supporting client data platforms and downstream reporting tools.
• - **Recruitment process:**
• - Technical interview — 45 minutes
• - Screening interview with the client's hiring manager — 30 minutes
• - Client technical interview — 45 minutes
• - Gigster states that referrals increase the chances of interviewing by 2x.