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$243.5K

How much do data engineer project jobs pay per year?

As of May 30, 2026, the average yearly pay for data engineer project 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.

What are the key skills and qualifications needed to thrive as a Data Engineer, and why are they important?

To thrive as a Data Engineer, you need strong proficiency in programming (Python, Java, or Scala), data modeling, and database management, often supported by a degree in computer science or a related field. Familiarity with big data tools (like Hadoop, Spark), ETL systems, cloud platforms (AWS, Azure, GCP), and relevant certifications is highly beneficial. Analytical thinking, problem-solving, and effective communication are crucial soft skills for collaborating with data teams and stakeholders. These competencies are essential for building reliable data pipelines and ensuring data availability and quality to drive business insights.

What are some common challenges faced by Data Engineers working on project-based teams?

Data Engineers on project-based teams often encounter challenges such as integrating data from disparate sources, ensuring data quality and consistency, and meeting tight project deadlines. Collaboration with data scientists, analysts, and software engineers is crucial, requiring clear communication to translate business needs into robust data pipelines. Additionally, adapting to evolving technologies and toolsets is essential for the successful delivery of scalable and maintainable solutions.

What is a Data Engineer Project?

A Data Engineer Project refers to a specific initiative or assignment undertaken by data engineers to design, build, and maintain systems that gather, process, and store large volumes of data. These projects often involve creating data pipelines, integrating multiple data sources, ensuring data quality, and optimizing storage solutions for analytics or business intelligence. Such projects are critical for organizations to manage their data efficiently and enable data-driven decision-making. Data Engineer Projects can range from building a data warehouse to implementing real-time data streaming solutions.

What is the difference between Data Engineer Project vs Data Engineer?

AspectData Engineer ProjectData Engineer
CredentialsTypically requires a degree in Computer Science, Data Science, or related fields; certifications like AWS, Google Cloud, or Azure are commonSimilar credentials; often holds certifications in cloud platforms and data tools
Work EnvironmentProject-based, often temporary teams working on specific data solutionsFull-time role within organizations, maintaining ongoing data pipelines and infrastructure
Industry UsageUsed across industries for specific data initiativesCore role in data-driven companies and departments
Search & Comparison IntentOften searched for project-based roles or freelance opportunitiesMore common in job searches for permanent positions

In summary, Data Engineer Projects focus on temporary, goal-specific data tasks, while Data Engineers hold ongoing roles responsible for maintaining data infrastructure. Both roles require similar skills and certifications but differ mainly in scope and employment type.

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What states have the most Data Engineer Project jobs? States with the most job openings for Data Engineer Project jobs include:
What job categories do people searching Data Engineer Project jobs look for? The top searched job categories for Data Engineer Project jobs are:
Data Engineer Project jobs near you
Data Engineer - Project Delivery Specialist

Data Engineer - Project Delivery Specialist

Deloitte

Boston, MA • On-site

Full-time

This job post has expired today. Applications are no longer accepted.

Deloitte rating

8.1

Company rating: 8.1 out of 10

Based on 86 frontline employees who took The Breakroom Quiz

59th of 138 rated financial services

Job description

Job Summary:
Deloitte is a leading professional services firm seeking an experienced Data Engineer - Project Delivery Specialist to join their Project Delivery Talent Model. The role involves designing, building, and operating modern data products and platforms while collaborating with business product teams to deliver reliable data pipelines and curated datasets for analytics.
Responsibilities:
• Architect, build, and operate scalable batch and near-real-time data pipelines on AWS.
• Design robust ingestion patterns from source systems into S3 and into Snowflake.
• Develop transformation layers and curated datasets in Snowflake, including dimensional/data product modeling for analytics and downstream applications.
• Implement orchestration and workflow automation on AWS with retries, backfills, and idempotency.
• Build reusable Python components for ingestion, validation, and transformations; enforce standards via code reviews and testing.
• Optimize Snowflake performance and cost warehouse sizing, concurrency patterns, query tuning, clustering/micro-partition considerations, and workload isolation.
• Partner with stakeholders to translate requirements into well-defined datasets and data contracts.
• Communicate regularly with Engagement Managers (Directors), project team members, and representatives from various functional and / or technical teams, including escalating any matters that require additional attention and consideration from engagement management.
• Independently and collaboratively lead client engagement workstreams focused on improvement, optimization, and transformation of processes including implementing leading practice workflows, addressing deficits in quality, and driving operational outcomes.
Qualifications:
Required:
• 7+ years of experience as a Data Engineer delivering production-grade data pipelines and curated datasets.
• 7+ years of hands-on experience with SQL and Python, including Snowflake and/or PySpark for scalable data processing and ELT.
• 7+ years of experience designing, building, and operating batch and near-real-time data pipelines on cloud platforms (AWS preferred; Azure/GCP acceptable).
• Experience with data integration frameworks and orchestration tools.
• Proficiency in designing and implementing Lakehouse/warehouse architectures and ELT patterns.
• Knowledge of DevOps principles: CI/CD pipelines, version control, Infrastructure-as-Code.
• Ability to optimize data storage, partitioning, file formats (Delta, Parquet), and performance.
• Understanding of data quality, data governance, and metadata management.
• Bachelor's degree, preferably in Computer Science, Information Technology, Computer Engineering, or related IT discipline; or equivalent experience.
• Limited immigration sponsorship may be available.
• Ability to travel 10%, on average, based on the work you do and the clients and industries/sectors you serve.
Preferred:
• Agile delivery experience (5-10 years).
• Analytical ability to manage multiple projects and prioritize tasks into manageable work products.
• Can operate independently or with minimum supervision.
• Excellent written and communication skills.
• Ability to deliver technical demonstrations.
Company:
Deloitte is a business consulting company that offers audit, consulting, financial advisory, and tax services. Founded in 1845, the company is headquartered in London, GBR, with a team of 10001+ employees. The company is currently Late Stage.

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