Data Engineer 2

tsu.edu; Houston (TX); Hybrid; USD 120,000 - 150,000; Full time

Job summary

tsu.edu is seeking a Data Engineer 2 to build scalable data pipelines and enterprise data assets. You will collaborate with BI analysts, governance professionals, and stakeholders to deliver trusted data across domains and support analytics, reporting, and ML initiatives.

The role offers opportunities across multiple business domains and contributes to modernization of the enterprise data ecosystem, with a hybrid work-from-home option after onboarding.

Qualifications

• Bachelor's degree in computer science, information systems, data analytics, engineering, or a related field.
• At least 5 years of experience building and maintaining scalable data pipelines and data integration.
• At least 5 years of experience delivering data warehouse, analytics, or BI solutions in an enterprise environment.
• Strong proficiency in SQL and data transformation techniques, with experience optimizing data ingestion, processing, and storage.
• Experience with cloud data platforms, data lakes, data warehouses, or lakehouse architectures.

Responsibilities

• Design, develop, test, and maintain scalable data pipelines that acquire, transform, and deliver data from multiple source systems to enterprise data platforms.
• Build and support enterprise data assets, including data warehouses, data lakes, lakehouses, curated datasets, and semantic-layer source tables.
• Develop and optimize ELT/ETL processes to ensure reliable data movement across the data ecosystem.
• Partner with BI analysts, data modelers, governance professionals, and stakeholders to translate requirements into scalable data solutions.
• Analyze source systems and processes to identify opportunities for improving data availability, quality, consistency, and usability.
• Implement and maintain data quality controls, monitoring, and validation procedures to ensure data integrity.
• Troubleshoot and resolve data pipeline failures, performance bottlenecks, and production issues.
• Design and implement data models and structures to support reporting, analytics, ML, and operational use cases.
• Optimize data storage, processing, and query performance for efficiency and scalability.
• Collaborate with Network Infrastructure, DevOps, and Security to meet enterprise standards and regulations.
• Evaluate and adopt new data engineering tools and best practices.
• Contribute to project planning, estimates, and technical decisions for data initiatives.
• Understand and adhere to all bank policies, laws and regulations applicable to their role.

Skills

SQL proficiency

Data modeling

ETL/ELT

Cloud data platforms

CI/CD practices

Collaboration

Education

Bachelor's degree in CS/IS/Analytics/Engineering

Tools

Airflow

dbt

SQL Server

Job description

Position Summary:

As a Data Engineer 2, you'll help build the foundation that enables data-driven decision-making across the organization. Working as part of a collaborative Data Insights Team, you'll develop scalable data pipelines, enterprise data assets, and innovative data solutions that empower reporting, analytics, and advanced business insights.

This role offers the opportunity to work across a wide range of business domains partner with technical and business stakeholders, and contribute to the ongoing modernization of our enterprise data ecosystem. You'll play a critical role in delivering trusted data that helps drive strategic initiatives and business outcomes.

Essential Functions:

• Design, develop, test, and maintain scalable data pipelines that acquire, transform, and deliver data from multiple source systems to enterprise data platforms.
• Build and support enterprise data assets, including data warehouses, data lakes, lakehouses, curated datasets, and semantic-layer source tables.
• Develop and optimize ELT/ETL processes ensure the reliable, timely, and accurate movement of data across the organization's data ecosystem.
• Partner with business intelligence analysts, data modelers, data governance professionals, and business stakeholders to translate business requirements into scalable data solutions.
• Analyze source systems and business processes to identify opportunities for improving data availability, quality, consistency, and usability.
• Implement and maintain data quality controls, monitoring, processes, and validation procedures to ensure the integrity and trustworthiness of enterprise data assets.
• Troubleshoot and resolve data pipeline failures, performance bottlenecks, data anomalies, and production support issues.
• Design and implement data models and structures that support reporting, analytics, machine learning, and operational data use cases.
• Optimize data storage, processing, and query performance to improve system efficiency and scalability.
• Collaborate with Network Infrastructure, DevOps, and Network Security teams to ensure solutions meet enterprise standards and regulatory requirements.
• Participate in the evaluation, implementation, and adoption of new data engineering tools, technologies, and best practices.
• Contribute to project planning, effort estimation, solution design, and technical decision-making for data and analytics initiatives.
• Understand and adhere to all bank policies, laws and regulations applicable to their role. Complete compliance training; follow internal processes and controls as required.
• Report all compliance issues, violations of law or regulations in accordance with the steps defined in bank policies.
• Performs other job-related duties or special projects as assigned.
• Regular and reliable attendance is an essential function of this position.

Qualifications:

• Bachelor's degree in computer science, information systems, data analytics, engineering, or a related field.
• At least 5 years of experience building and maintaining scalable data pipelines and data integration, solutions.
• At least 5 years of experience delivering data warehouse, analytics, or business intelligence solutions in an enterprise environment.
• Strong proficiency in SQL and data transformation techniques, with experience optimizing data ingestion, processing, and storage.
• Experience working with relational dimensional, and analytical data models.
• Experience with cloud data platforms, data lakes, data warehouses, or lakehouse architectures preferred.
• Experience using source control, automated testing, CI/CD practices, and DevOps methodologies preferred.
• Experience supporting reporting, business intelligence, self-service analytics, or AI/ML data initiatives preferred.
• Financial services or other regulated industry experience preferred.

Preferred Talents:

• Effective written and verbal communication
• Strong analytical, research, and problem-solving skills
• Attention to detail and commitment to data quality
• Organization and effective prioritization
• Positive, collaborative, and customer-focused approach

Working Environment:

Indoor work – not exposed to outdoor elements or hazards.

Some sedentary work and occasional lifting and/or carrying up to 25 pounds.

This role is eligible for hybrid work from home opportunity under the work from home guidelines upon completion of onboarding.

PLEASE VIEW THE APPLICANT TERMS & CONDITIONS BEFORE PROCEEDING WITH YOUR APPLICATION.