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Data Engineering Jobs (NOW HIRING)

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Manager, Data Engineering

Manager of Data Engineering Location: Toronto/GTA Company Overview: Our client is a premier Data Cloud and Business Intelligence consulting company specializing in helping businesses harness the ...

AlloBaas

Data Engineering Lead

Lakewood, OH · On-site

The Data Engineering Lead plays a critical and strategic role in advancing FMHC's enterprise data transformation and analytics enablement efforts. This role provides technical leadership, direction ...

CRH

Manager, Data Engineering

Atlanta, GA · On-site

The Manager, Data Engineering will facilitate the definition of data solutions, technical architecture, and delivery plans while ensuring high standards of data quality, governance, and security.

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How much do data engineering jobs pay per year?

As of Jul 7, 2026, the average yearly pay for data engineering in the United States is $165,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $133,500.00 and $170,000.00 per year, depending on experience, location, and employer.

Is AI replacing data engineers?

AI is automating certain tasks within data engineering, such as data cleaning and pipeline management, but it does not replace the need for data engineers. Data engineers are essential for designing, building, and maintaining complex data systems, and their expertise in tools like SQL, Spark, and cloud platforms remains critical for managing data workflows and ensuring data quality.

What work does a data engineer do?

A data engineer designs, builds, and maintains data pipelines and infrastructure to collect, store, and process large volumes of data. They work with tools like SQL, Python, and cloud platforms to ensure data is accessible, reliable, and optimized for analysis by data scientists and analysts.

What are the typical daily responsibilities of a Data Engineer?

Data Engineers regularly design, build, and maintain scalable data pipelines to support analytics and business intelligence teams. Their daily tasks often involve working with large datasets, optimizing data storage, ensuring data integrity, and troubleshooting data-related issues. Collaboration with data scientists, analysts, and software engineers is common to align on data requirements and improve workflows. You may also participate in regular code reviews and contribute to the ongoing improvement of data infrastructure. This role is ideal for problem-solvers who enjoy working with both code and complex systems in a collaborative, fast-paced environment.

What engineers make 500,000?

Senior data engineers with extensive experience, specialized skills in cloud platforms, and advanced knowledge of data architecture can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires a combination of technical expertise, leadership roles, and sometimes stock options or bonuses.

What is a Data Engineering job?

A Data Engineering job involves designing, building, and maintaining the infrastructure that enables efficient data collection, storage, and processing. Data Engineers develop pipelines to transform raw data into usable formats for analytics and machine learning. They work with databases, big data technologies, and cloud platforms to ensure data is accessible and reliable. Their role is crucial for organizations to make data-driven decisions and optimize business processes.

Are data engineers still in demand?

Data engineers are currently in high demand due to the increasing reliance on data-driven decision making and the growth of big data technologies. They typically require skills in SQL, cloud platforms, and data pipeline tools like Apache Spark or Kafka, making their expertise valuable across many industries. The role is expected to remain strong as organizations continue to prioritize data infrastructure and analytics capabilities.

What are the key skills and qualifications needed to thrive in the Data Engineering position, and why are they important?

To thrive in Data Engineering, you need a solid background in programming (such as Python, Java, or Scala), data modeling, and database management, typically supported by a degree in computer science or a related field. Familiarity with ETL tools, cloud platforms like AWS or Azure, big data frameworks (e.g., Hadoop, Spark), and relevant certifications is highly valued. Strong problem-solving abilities, effective communication, and the ability to work collaboratively across teams are key soft skills for this role. These attributes are crucial for designing robust data pipelines, ensuring data quality, and enabling organizations to make data-driven decisions efficiently.

More about Data Engineering jobs
What cities are hiring for Data Engineering jobs? Cities with the most Data Engineering job openings:
What are the most commonly searched types of Data Engineering jobs? The most popular types of Data Engineering jobs are:
What states have the most Data Engineering jobs? States with the most job openings for Data Engineering jobs include:
What job categories do people searching Data Engineering jobs look for? The top searched job categories for Data Engineering jobs are:
Data Engineering jobs near you
Infographic showing various Data Engineering job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 12% Part Time, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $165,018 per year, or $79.3 per hour.
Data Engineering Manager

Data Engineering Manager

Bollinger Shipyards

Raceland, LA • On-site

Full-time

Posted 19 days ago

Bollinger Shipyards rating

5.5

Company rating: 5.5 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

Job description

Job Title: Data Engineering Manager
Location: Multiple Location
Position Overview: The Data Engineering Manager leads the design, development, and delivery of enterprise data pipelines and foundational data assets supporting analytics, reporting, forecasting, and AI initiatives. This role is responsible for establishing scalable and reliable data engineering practices while ensuring enterprise data is accurate, secure, accessible, and aligned to business priorities.
The role partners closely with Enterprise Architecture, Analytics, Data Science, Infrastructure, and business stakeholders to integrate data from ERP, operational, engineering, and manufacturing systems into the enterprise Azure data platform.
Key Responsibilities:
• Lead the development and support of enterprise data pipelines and integrations across Oracle ERP, Finesse, MES, PLM, Primavera, proposal systems, and other operational platforms
• Establish and maintain scalable data engineering standards, frameworks, and development practices
• Ensure consistent implementation of enterprise data architecture and medallion data design patterns (raw, curated, business-ready)
• Oversee data ingestion, transformation, orchestration, reconciliation, and validation processes
• Partner with Enterprise Architecture and Integration teams to align technical solutions with enterprise standards and future-state architecture
• Support analytics, reporting, forecasting, and AI initiatives through delivery of trusted and performant datasets
• Ensure data quality, lineage, observability, and reliability across enterprise data assets
• Manage priorities, sprint planning, delivery timelines, and technical execution for the data engineering team
• Collaborate with cybersecurity and infrastructure teams to ensure secure and compliant handling of enterprise data
• Evaluate emerging technologies and recommend improvements to data engineering capabilities and platform performance
• Mentor and develop technical talent while fostering strong engineering discipline and accountability
Qualifications:
• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Management, or related field
• 8-12 years of experience in data engineering
• Proven experience delivering enterprise-scale data pipelines
• Strong SQL and data transformation expertise
• Experience with cloud data platforms (Azure preferred)
• Experience integrating ERP and operational systems
Skills and Abilities:
• Experience with Azure Synapse, Azure Data Factory, Databricks, Fabric, or similar cloud technologies
• Experience in manufacturing, shipbuilding, industrial, or engineering-intensive environments
• Familiarity with Power BI and downstream analytics enablement
• Experience supporting AI/ML initiatives through engineered datasets and feature pipelines
• Knowledge of data observability, master data management, and metadata management practices
• Relevant cloud or data engineering certifications
Bollinger is an equal opportunity employer and is committed to providing employment opportunities to minorities, females, veterans and disabled individuals, and without regard to sexual orientation and gender identity.

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