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Data Modeling Jobs in Pennsylvania (NOW HIRING)

Data Modeler

Malvern, PA · On-site

$53.75 - $69.75/hr

Required Qualifications: • 8+ years of experience in enterprise data modeling and data architecture. • Proven experience within investment management, asset management, wealth management, or ...

Data Modeler

Malvern, PA · On-site

$53.75 - $69.75/hr

Apply data modeling best practices, including normalization, slowly changing dimensions (SCD), data lineage, metadata management, and reference data governance. * Develop and optimize data models ...

Data Modeler

Philadelphia, PA · On-site

$55.25 - $71.75/hr

Expert in relational and dimensional structures for large (multi-terabyte) operational, analytical, warehouse and BI systems Skilled in data profiling, business domain modeling, logical data modeling ...

Sr. Data Modeler

Philadelphia, PA

$55.25 - $71.75/hr

Collaborate on developing/document modeling standards, processes and best practices. * Adhere to the standards and provide recommendations for changes Required: * 8+ years of IT expericne in Data ...

Data Architect

Pittsburgh, PA · On-site

$60.50 - $78/hr

Database design, data modeling, data warehousing, SQL, ETL processes, cloud technologies, data mining. * Programming languages like Python and Java. Roles & Responsibilities * Designing and ...

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Data Modeling information

See Pennsylvania salary details

$10

$58

$83

How much do data modeling jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for data modeling in Pennsylvania is $58.85, according to ZipRecruiter salary data. Most workers in this role earn between $52.79 and $68.41 per hour, depending on experience, location, and employer.

What is a data modeling?

A Data Modeling job involves designing and structuring data to ensure it is organized, efficient, and scalable for business needs. Data modelers create conceptual, logical, and physical data models that define relationships between data elements. They work closely with database administrators, data engineers, and analysts to optimize data storage and retrieval. Their role is crucial for maintaining data integrity and supporting business intelligence and analytics initiatives. Skills in SQL, database design, and data normalization are essential for success in this role.

What does a typical day look like for someone working in data modeling?

A typical day in Data Modeling often involves collaborating with business analysts, database administrators, and software developers to understand data requirements and translate them into logical and physical data structures. Data modelers spend time designing, reviewing, and optimizing data models, ensuring accuracy and consistency across systems and projects. They also review data flows, document data dictionaries, and participate in meetings to align data architecture with overall business needs. The role frequently requires balancing independent technical work with teamwork, as well as responding to feedback and evolving project requirements to support organizational goals.

What are the key skills and qualifications needed to thrive in data modeling, and why are they important?

To thrive in Data Modeling, you need strong analytical skills, proficiency in database design, and a solid understanding of data structures, usually supported by a degree in computer science, information systems, or a related field. Expertise with tools such as ERwin, SQL, PowerDesigner, or similar data modeling software, as well as knowledge of normalization techniques and experience with data warehousing concepts, are highly valued. Effective communication, attention to detail, and problem-solving abilities set outstanding data modelers apart, allowing them to convey complex concepts to both technical and non-technical stakeholders. These skills are vital for building accurate, scalable data models that serve as the foundation for reliable data-driven decision-making within organizations.

How much do data modelers make?

Data modelers typically earn a median annual salary between $80,000 and $120,000, depending on experience, location, and industry. Senior data modelers with advanced skills in database design and data warehousing can earn higher salaries, often exceeding $130,000. Certifications in data management and proficiency with tools like SQL and ER modeling can also influence compensation.

Is data modeling a good career?

Data modeling is a valuable career in data management and analytics, involving designing and organizing data structures for databases and systems. It requires skills in database tools, understanding of business requirements, and often benefits from certifications like CBIP or data modeling tools such as ERwin or PowerDesigner. The role offers opportunities in various industries with a focus on data quality and efficiency.

Is data modeling hard to learn?

Data modeling can be challenging for beginners due to the need to understand database structures, relationships, and normalization concepts. However, with consistent study, practice, and familiarity with tools like ER diagrams and SQL, many learners can develop proficiency over time.

What are the most commonly searched types of Data Modeling jobs in Pennsylvania?

The most popular types of Data Modeling jobs in Pennsylvania are:

Infographic showing various Data Modeling job openings in Pennsylvania as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 17% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $122,416 per year, or $58.9 per hour.

$53.75 - $69.75/hr

Other

Posted 5 days ago


Job description

Job Title

Sr. Data Modeler

Location

Malvern, PA – Hybrid – 3 days a week

Duration

6 months + Possible extension

Interview Mode

Video 2 rounds

 

 

2 manager references with LinkedIn

Key Responsibilities:

• Design, develop, and maintain conceptual, logical, canonical, and physical data models, including ERDs and dimensional/star schemas, to support asset management, wealth management, and financial advisory platforms.

• Partner with business stakeholders, data engineers, solution architects, and technical teams to translate complex financial domain requirements into scalable, enterprise data models.

• Model data across key financial subject areas, including financial profiles, advisor teams, securities, holdings, portfolio performance, and risk analytics.

• Apply data modeling best practices, including normalization, slowly changing dimensions (SCD), data lineage, metadata management, and reference data governance.

• Develop and optimize data models across relational databases, cloud platforms, and big data environments such as Snowflake, SQL Server, AWS, and Azure.

• Ensure data models comply with financial services regulatory requirements, including data privacy, auditability, and reference data integrity.

• Collaborate with ETL and data engineering teams to validate source-to-target mappings, troubleshoot data quality issues, and support reporting and analytics initiatives.

Required Qualifications:

• 8+ years of experience in enterprise data modeling and data architecture.

• Proven experience within investment management, asset management, wealth management, or broader financial services environments.

• Strong understanding of conceptual, logical, canonical, and physical data modeling methodologies.

• Hands-on experience designing dimensional (star schema) and normalized relational data models.

• Advanced SQL skills with the ability to analyze, validate, and optimize complex datasets.

• Proficiency with enterprise data modeling tools such as Erwin, ER/Studio, Hackolade, or similar platforms.

• Experience working with cloud data platforms and databases, including Snowflake, SQL Server, AWS, and Azure.

• Knowledge of data governance, metadata management, data lineage, and slowly changing dimension (SCD) concepts.

• Experience partnering with cross-functional teams to translate business requirements into scalable data solutions.

• Familiarity with financial services regulatory and compliance requirements related to data privacy, audit trails, and data integrity.