ZipRecruiter

Log In

1

Senior Data Pipeline Engineer Jobs (NOW HIRING)

Worley

Senior Pipeline Engineer

Denver, CO · On-site

Senior Pipeline Engineer Play an integral role in Worley's Pipeline Systems team, helping deliver the critical infrastructure that supports energy, chemicals, resources, and emerging energy markets ...

Immersion Consulting

Senior Data Engineer

$108K - $147K/yr

The Senior Data Engineer develops and maintains modern ELT/ETL pipelines, data models, source-controlled environments, and operational standards that enable efficient data ingestion, processing ...

Lorven Technologies

Sr.Data Engineer

$117K - $140K/yr

Job Title: Sr.Data Engineer Location: Remote Duration: Contract : About the Role We are looking for ... In this role, you will design, build, and maintain scalable data pipelines and infrastructure on ...

previous page

Showing results 41-60

All JobsTechnology JobsData JobsData Software Engineer JobsSenior Data Pipeline Engineer Jobs

Senior Data Pipeline Engineer information

See salary details

$81K

$126.3K

$175K

How much do senior data pipeline engineer jobs pay per year?

As of Sep 6, 2026, the average yearly pay for senior data pipeline engineer in the United States is $126,328.00, according to ZipRecruiter salary data. Most workers in this role earn between $106,000.00 and $144,000.00 per year, depending on experience, location, and employer.

What is a senior data pipeline engineer?

A Senior Data Pipeline Engineer is a highly skilled professional responsible for designing, building, and maintaining the systems that move and process large amounts of data within an organization. They ensure data is collected, transformed, and made available to analysts, data scientists, and business applications in a reliable and efficient manner. Senior engineers typically lead projects, mentor junior staff, and make critical decisions about data architecture and technology choices. Their work is essential for organizations that rely on data-driven insights and operations.

What are some typical challenges senior data pipeline engineers face when ensuring data quality and reliability?

Senior Data Pipeline Engineers frequently encounter challenges such as handling large volumes of diverse data, ensuring timely and accurate data delivery, and implementing robust monitoring to detect failures early. Managing data consistency across distributed systems and balancing real-time processing with batch workloads are also common hurdles. Collaborating closely with data scientists, analysts, and DevOps teams is essential to resolve data quality issues and maintain reliable pipelines in a dynamic environment.

What are the key skills and qualifications needed to thrive as a senior data pipeline engineer, and why are they important?

To thrive as a Senior Data Pipeline Engineer, you need strong expertise in data engineering, programming (Python, Java, or Scala), and a solid understanding of ETL processes, often backed by a degree in computer science or a related field. Familiarity with big data tools such as Apache Spark, Kafka, Airflow, and cloud platforms like AWS or GCP, as well as relevant certifications, is typically required. Excellent problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and address complex data challenges. These skills and qualifications are crucial for building robust, scalable, and efficient data pipelines that support critical business analytics and decision-making.
More about Senior Data Pipeline Engineer jobs

What cities are hiring for Senior Data Pipeline Engineer jobs?

Cities with the most Senior Data Pipeline Engineer job openings:

What are the most commonly searched types of Data Pipeline Engineer jobs?

The most popular types of Data Pipeline Engineer jobs are:

What states have the most Senior Data Pipeline Engineer jobs?

States with the most job openings for Senior Data Pipeline Engineer jobs include:

What job categories do people searching Senior Data Pipeline Engineer jobs look for?

The top searched job categories for Senior Data Pipeline Engineer jobs are:

Senior Data Pipeline Engineer jobs near you
Infographic showing various Senior Data Pipeline Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $126,328 per year, or $60.7 per hour.

Data Pipeline Engineer with Security Clearance

Kforce Federal Solutions

Washington, DC • On-site

$131K - $157K/yr

Other

Re-posted 13 days ago

Key responsibilities

  • Investigate and resolve data pipeline failures across multiple production environments.

  • Perform root cause analysis on data quality and pipeline performance issues and apply targeted code fixes.

  • Monitor pipeline health, respond to alerts within defined SLAs, and support existing ETL processes.

Job description

Data Engineer – Pipeline Operations & Incident Response Overview
This role is heavily focused on maintaining and stabilizing large-scale data pipelines in a production environment. The majority of time is spent troubleshooting and resolving issues across existing data workflows rather than building new systems.
Early success in this position looks like gaining enough familiarity with the platform, data flows, and key stakeholders to independently diagnose and resolve pipeline failures across multiple environments. Key Responsibilities Investigate and resolve data pipeline failures across multiple production environments
Perform root cause analysis on data quality and pipeline performance issues
Apply targeted code fixes and adjustments to restore pipeline functionality
Monitor pipeline health and respond to alerts within defined SLAs
Support and maintain existing ETL processes rather than developing new ones
Refactor pipelines to resolve performance issues such as memory constraints or inefficient processing
Coordinate with upstream data providers and internal teams to resolve data ingestion issues
Escalate issues when access, ownership, or dependencies fall outside immediate control Day-to-Day Breakdown ~85–90%: Debugging, incident response, and pipeline issue resolution
~5–10%: Monitoring, validation, and health checks
~5–10%: Minor code updates, optimizations, and pipeline adjustments Work is centered on fixing and stabilizing existing pipelines, not building new ones from scratch. Technical Environment Predominantly batch-based ETL pipelines (incremental processing is common)
High-volume pipeline ecosystem spanning multiple data domains and environments
Mix of code-driven pipelines and low-code/visual pipeline tools
Streaming pipelines are minimal Required Technical Skills Strong experience with large-scale data engineering and ETL/ELT workflows
Proficiency in Python and distributed data processing frameworks (PySpark preferred)
Solid understanding of dataframes and data manipulation at scale
Experience troubleshooting production data pipelines and debugging failures
Knowledge of relational databases and SQL fundamentals
Familiarity with distributed computing concepts Additional Technical Exposure Experience with Java or similar languages (C++ acceptable alternative)
Ability to diagnose and resolve memory/performance issues in distributed jobs
Exposure to visual pipeline tools or data workflow platforms is helpful
Basic understanding of networking concepts and API-based data ingestion Operational Environment Engineers support a large number of pipelines across multiple environments simultaneously
Work is highly reactive, driven by incoming alerts and data incidents
Engineers are expected to quickly assess and troubleshoot pipelines they have not previously worked on
High alert volume, with multiple issues often tied to common root causes Collaboration Frequent interaction with data providers to resolve source data issues
Regular coordination with cross-functional technical teams on pipeline failures
Occasional engagement with end users reporting data discrepancies On-Call & Incident Response Rotating on-call schedule supporting different pipeline groups
Some rotations may include off-hours alerts tied to overnight pipeline processing
Majority of incidents handled during business hours, with occasional escalation scenarios
Engineers are expected to own resolution when possible and coordinate when dependencies exist Ideal Candidate Background Strong foundation in data engineering within production environments
Experience supporting operational data systems rather than purely building new solutions
Comfortable working in high-volume, incident-driven environments
Able to quickly understand and troubleshoot unfamiliar systems
Hands-on experience with distributed data processing and large datasets

Apply