Data Engineer

At Franklin Templeton, we believe success is built through powerful partnerships. As a forward thinking asset manager, we build dynamic relationships with clients, understand their goals, and navigate complex markets together. We leverage cutting edge strategies and deep insights to unlock opportunities for long term wealth creation. Our talented, global teams bring expertise that is both broad and unique.

From our welcoming, inclusive, and supportive culture to our globally diverse business, we offer opportunities not only to help you reach your potential, but also to contribute to our clients’ success.

Data Engineer About the Department The Client Data Services (CDS) team is a strategic data function within Enterprise Data Analytics that directly enables revenue growth, client engagement, and better decision-making through trusted data. The team delivers high-quality, governed client datasets that power sales strategy, analytics, and distribution intelligence across the firm.

CDS operates as a cross-functional team combining data engineers, software engineers, and analysts who partner closely with Sales, Marketing, and business stakeholders. Work is highly collaborative, fast-paced, and outcome-driven.

Joining this team provides the opportunity to work on enterprise-scale data platforms, modern cloud technologies, and AI-enabled workflows, while continuously building technical depth and business context.

Job Overview We are seeking a Data Engineer to design, build, and optimize scalable data platforms across transaction systems and cloud-based lakehouse architectures. This role is central to building a governed, high-performance data ecosystem that supports analytics and AI-driven use cases.

You will collaborate with global teams to translate business requirements into reliable, scalable data solutions.

How you will add value

Build and own data products with clearly defined SLAs, quality metrics, and consumer-facing interfaces (APIs, reports, or datasets) used for sales and analytics decision making.

Develop and optimize data processing using SQL, PL/SQL, and Python for large-scale datasets.

Improve pipeline performance and manage cost through efficient design and optimization.

Partner with stakeholders to translate requirements into scalable data solutions.

Ensure data quality by embedding validation, monitoring, and governance controls.

Troubleshoot issues and drive root cause resolution to improve system reliability.

Implement automated testing, validation, and CI/CD pipelines to ensure reliable deployment of data pipelines and services.

Define and enforce data/API contracts to ensure consistency, reliability, and backward compatibility across systems.

AI Fluency We expect the candidate to apply an AI-first mindset in both development workflows and system design.

Use AI tools for coding, testing, debugging, and documentation while validating outputs.

Incorporate AI into system design and evaluate trade-offs.

Automate repetitive workflows using AI-powered solutions.

Apply structured prompting techniques to solve complex problems.

Ensure AI-assisted solutions are production-ready with testing and monitoring.

What will help you be successful in this role

Requirements

Education:

Bachelor’s degree in Computer Science, Engineering, or related field.

Work Experience:

Experience (6+ years) designing and building enterprise data pipelines and platforms.

Technical Depth:
Ability to build or optimize data workflows, not just operate existing systems.

Collaboration:

Experience working with business stakeholders to deliver data solutions.

Reporting & BI:
Experience translating business requirements into scalable reports and dashboards using Business Objects or similar BI tools.

Technical Skills Strong proficiency in SQL, PL/SQL and data modeling for large datasets.

Experience with cloud platforms (AWS) and relational/distributed databases (e.g., Oracle).Programming skills in Python and/or Spark for data processing.

Experience with data pipeline tools (Informatica or equivalent).

Experience with orchestration tools such as Control-M or Airflow.

Experience designing and delivering business-facing reports/dashboards using Business Objects (or similar BI tools) based on large, governed datasets.

Experience with version control (Git) and CI/CD pipelines for automated build, test, and deployment.

Experience with data observability, monitoring, and reliability practices (e.g., SLA tracking, anomaly detection, lineage).Soft Skills Able to communicate complex data…