Senior Data Engineer at THORNBURG INVESTMENT MANAGEMENT INC

Cuyamungue, New Mexico, United States -

Full Time

Start Date

Immediate

Expiry Date

16 Jul, 26

Salary

0.0

Posted On

17 Apr, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data engineering, Snowflake, ETL strategies, Data modeling, Performance tuning, SQL, Cloud data pipelines, Python, Data architecture, Business intelligence, Machine learning, Atlassian Jira, Confluence, Git, Agile, Kanban

Industry

Financial Services

Description

Description Thornburg is a global investment firm delivering on strategy for institutions, financial professionals and investors worldwide. The privately held firm, founded in 1982, is an active, high-conviction manager of fixed income, equities, and multi-asset solutions. As an independent firm, Thornburg can take on a wide range of opportunities, explore ideas thoroughly and work across strategies to deliver consistent risk-adjusted outperformance over the long term. The firm attracts free-thinking professionals who are eager to pursue investment outcomes beyond the confines of popular wisdom. From nimble operational capabilities to principles and actions fitting of a global citizen, Thornburg’s world-class investment platform and team are aligned on strategy to serve investors. Primary Job Function The Senior Data Engineer works as an integral member of the Data Architecture & Engineering team to design, build, and optimize solutions for data collection, storage, and access at scale. Using technology and asset management industry knowledge, the Senior Data Engineer collaborates with stakeholders to improve data models and pipelines by delivering solutions that increase data accessibility and foster data-driven decision making throughout the organization. Duties and Responsibilities Design, build, test, and maintain on-premises and cloud-based data pipelines to acquire, profile, cleanse, consolidate, integrate, persist, and expose structured, semi-structured, and unstructured data. Influence long term data strategies and advocate for technology that improves delivered data quality and enablement. Leverage AI capabilities and technologies to accelerate development and improve data quality. Generate accessible data that supports business intelligence, reporting, exploratory data analysis, statistical analysis, predictive analytics, and machine learning models. Explore and employ a variety of programming languages, tools, and interfaces to integrate systems and deliver reliable data. Remain current on data architecture, analytics principles while researching new technologies, AI capabilities and processes to improve data acquisition and accessibility. Ensure systems meet industry best practices considering security, privacy, classification, performance, and retention requirements. Understand, design, and describe complex database structures. Curate, analyze, and validate data sets. Optimize and tune queries for performance. Document and effectively communicate technical solutions. Lead and participate in design and code review sessions. Collaborate with team members to continuously improve departmental standards, processes, and documentation. Mentor junior analysts and engineering staff. Perform other job duties assigned as necessary. Requirements Bachelor’s degree in computer science or related field. Comparable work experience may be substituted for education requirements. 10+ years developing enterprise data solutions (ETL strategies, data modeling, performance tuning). 2+ years of development on Snowflake cloud database technologies; familiarity with or demonstrated ability to learn emerging Snowflake capabilities including dbt, Openflow, and Cortex Code is preferred. Previous experience developing with Microsoft technologies (SQL, SSIS, SSAS, SSRS, Power BI) is preferred. Previous experience in the financial services industry with proven domain knowledge. Professional experience with issue tracking, collaboration tools, and platforms such as Atlassian Jira and Confluence. Professional experience using source control tools such as Bitbucket or GitHub, including branching and merging workflows. Advanced knowledge of IT environments including networking, server technologies, databases, FTP, security, testing, and deployments. Proven ability to evaluate requirements and estimate project tasks through inclusive discussions with stakeholders and colleagues. The ability to work as part of a team, which may be established for a chartered project or as part of ongoing daily Agile/Kanban assignments within the department. Highly motivated to learn new technologies and contribute to the improvement of department processes, standards, and documentation. Problem solver with the ability to think outside the box. Demonstrate flexibility despite constraints to build consensus. Must be accountable and detail oriented with excellent communication skills, both oral and written. Commitment to complete assignments according to established goals and objectives. Job Benefits Medical, dental, and vision coverage. Employer 401(k) safe harbor and profit-sharing contributions. Work/life programs such as flexible work arrangements, flexible paid time off, paid parental leave, employee assistance, education reimbursement program. Community involvement opportunities. Onsite cafeteria. Onsite fitness center. Referral program. Santa Fe is the capital of New Mexico. Founded in 1610 and nicknamed "The City Different," Santa Fe captures hearts and minds with its museums, restaurants, theater, opera, and adobe-style architecture. With an average of over 300 sunny days per year and picturesque landscape, it's no wonder that outdoor activities such as skiing and hiking in "The Land of Enchantment" attract visitors from around the world. All qualified applicants will receive consideration for employment without regard to age, race, color, religion, sex, national origin, disability, gender, gender identity or protected veteran status.

Responsibilities

The Senior Data Engineer will design, build, and maintain scalable data pipelines and architectures to support data-driven decision-making. They will also collaborate with stakeholders to optimize data models and leverage AI technologies to improve data quality and accessibility.