Data Pipeline Engineer Jobs

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

Salary

175,000 – 275,000 per year