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Dbt Data Engineer Jobs in Delaware (NOW HIRING)

Photon

Data Engineer Lead

Newark, DE · On-site

Must-Have Qualifications 8+ years in data engineering, with 3+ years as a tech lead owning end-to ... Experience building data-quality frameworks with tools like Great Expectations or dbt tests.

Photon

Lead Data Engineer | Onsite - Delaware

Wilmington, DE · On-site

$111K - $133K/yr

Report delivery status, risks, and blockers to engineering leadership. Must-Have Qualifications ... Experience building data-quality frameworks with tools like Great Expectations or dbt tests.

Net2Source (N2S)

Power BI Architect

Wilmington, DE · On-site

... Engineering Collaboration • Partner with data engineering teams to implement: • ETL/ELT ... Fabric / Synapse • Data integration: Informatica / ADF / Fivetran / dbt / Airflow (any relevant ...

All JobsDbt Data Engineer JobsDbt Data Engineer Jobs in Delaware

Dbt Data Engineer information

Is dbt in demand?

Yes, dbt Data Engineers are in demand as organizations increasingly adopt modern data transformation tools to improve data workflows. Skills in SQL, data modeling, and familiarity with cloud platforms enhance job prospects in this field.

What engineer makes $500,000 a year?

Highly experienced data engineers, including those working with advanced big data tools like Apache Spark and cloud platforms, can earn salaries approaching or exceeding $500,000 annually, especially in senior or specialized roles at large tech companies. Achieving this level typically requires extensive expertise, certifications, and a strong track record in data architecture and engineering.

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

To thrive as a Dbt Data Engineer, you need strong SQL skills, experience in data modeling, and a solid understanding of ELT/ETL pipelines, often supported by a degree in computer science or a related field. Familiarity with dbt (data build tool), version control systems like Git, and cloud data platforms such as Snowflake or BigQuery is typically required. Attention to detail, problem-solving abilities, and effective collaboration are essential soft skills for this role. These skills ensure robust, scalable, and maintainable data transformations that drive reliable analytics and business insights.

Are data engineers still in demand?

Data engineers are currently in high demand due to the increasing need for managing large-scale data pipelines, cloud platforms, and data integration tools. Skills in SQL, Python, and cloud services like AWS or Azure enhance job prospects in this field.

How does a Dbt Data Engineer typically collaborate with data analysts and other stakeholders?

As a Dbt Data Engineer, you'll work closely with data analysts, business intelligence teams, and sometimes product managers to translate business requirements into reliable, well-structured data models. Collaboration often involves reviewing transformation logic, ensuring data quality, and providing documentation or training on Dbt models. You may also participate in regular stand-ups or data modeling sessions to align on priorities and address data challenges collaboratively. Effective communication skills are key, as you'll bridge the gap between raw data and actionable insights.

What are Dbt Data Engineers?

Dbt Data Engineers are professionals who specialize in using dbt (data build tool) to transform, test, and document data within modern data warehouses. They build and maintain data pipelines by writing SQL-based transformation scripts and ensuring data quality through automated testing. Dbt Data Engineers collaborate closely with analytics teams to create reliable, well-documented datasets that support business intelligence and analytics initiatives.

Do data engineers use dbt?

Data engineers often use dbt (data build tool) to transform and model data within data warehouses. It is a popular tool for managing data pipelines, enabling version control, testing, and documentation, which are key responsibilities of data engineers.

What is the difference between Dbt Data Engineer vs Data Analyst?

AspectDbt Data EngineerData Analyst
Primary FocusBuilding and maintaining data transformation pipelines using dbtAnalyzing data to generate reports and insights
Skills & ToolsSQL, dbt, ETL pipelines, cloud platformsSQL, Excel, BI tools, data visualization
Work EnvironmentData engineering teams, cloud data platformsBusiness units, reporting teams
CertificationsSQL, cloud certifications, dbt trainingData analysis, visualization certifications

While both roles work with data and SQL, Dbt Data Engineers focus on developing scalable data transformation pipelines using dbt, whereas Data Analysts primarily analyze data to produce reports and insights. The roles complement each other within data teams but differ in technical scope and responsibilities.

What are popular job titles related to Dbt Data Engineer jobs in Delaware? For Dbt Data Engineer jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching Dbt Data Engineer jobs in Delaware look for? The top searched job categories for Dbt Data Engineer jobs in Delaware are:
What cities in Delaware are hiring for Dbt Data Engineer jobs? Cities in Delaware with the most Dbt Data Engineer job openings:
Dbt Data Engineer jobs near you
Data Engineer Lead

Data Engineer Lead

Photon

Newark, DE • On-site

Other

Posted 16 days ago

Job description

Job Title :Technical Lead - Operational Data Governance & Modernization
Location :Newark, DE

Owns end-to-end delivery of how operational data is modeled, governed, and moved across the organization from relational/canonical models and master data through schema-first contracts into the enterprise warehouse. Sets the standards for data quality, compliance (PII), and "contract as code" governance, but is measured on delivery outcomes runs the offshore team day-to-day and stays hands-on to unblock delivery risk.
Description for Internal Candidates

Key Responsibilities
Own delivery of relational and canonical data models and the organization's MDM (master data management) strategy.
Set standards for advanced SQL usage and appropriate use of NoSQL patterns where relational modeling doesn't fit.
Define schema-first data contract design using JSON Schema/Avro/Protobuf.
Drive rollout of the data catalog and enforce PII classification and data-regulation compliance across pipelines.
Own delivery of the Snowflake/enterprise data warehouse and data pipelines between ODS and warehouse.
Drive delivery of data change detection (CDC) and the data validation/quality testing framework.
Own "contract as code" governance: data schema management via CI/CD and Git-based data governance.
Select and stand up appropriate AWS data services for the platform.
Run day-to-day delivery of the offshore team: sprint commitments, code/design review, real-time unblocking, and hands-on work on critical-path pipelines.
Report delivery status, risks, and blockers to engineering leadership.

Must-Have Qualifications
8+ years in data engineering, with 3+ years as a tech lead owning end-to-end delivery (not a pure design/review architect role).
Proven track record of shipping data platforms on committed timelines, including hands-on troubleshooting under delivery pressure.
Deep expertise in relational and canonical data modeling and MDM design.
Expert-level SQL and solid understanding of when NoSQL patterns are appropriate.
Proven experience designing schema-first data contracts (JSON Schema/Avro/Protobuf).
Experience architecting data catalog tooling and PII classification/data regulation compliance (critical given financial-services context).
Strong hands-on architecture experience with Snowflake or an equivalent enterprise data warehouse.
Proven design of data pipelines between operational data stores and the warehouse.
Experience architecting change-data-capture and data validation/quality frameworks.
Demonstrated experience implementing "contract as code" schema management via CI/CD, Git-based governance.
Solid knowledge of relevant AWS data services (e.g., RDS, Redshift, Glue, DMS, Lake Formation).
Financial-services or similarly regulated-industry data governance experience is strongly preferred.
Strong stakeholder communication; able to directly manage day-to-day delivery of an offshore team (standups, unblocking, sprint accountability).

Nice-to-Have Qualifications
Experience with specific data catalog tools (e.g., Collibra, Alation, AWS Glue Data Catalog).
Exposure to multiple warehouse platforms beyond Snowflake.
Experience building data-quality frameworks with tools like Great Expectations or dbt tests.
Relevant certifications (Snowflake SnowPro, AWS Data Analytics Specialty).

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