Engineering- Market Data Engineer- Associate at AQR

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

04 Jan, 26

Salary

0.0

Posted On

06 Oct, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Big Data, Cloud Computing, Python, Spark, Hadoop, Analytical Skills, Collaboration, Quantitative Finance, Debugging, Data Validation, AI Models, Data Management, Investment Insights, Research, Portfolio Management

Industry

Financial Services

Description

About AQR Capital India AQR is a global investment firm built at the intersection of financial theory and practical application. We strive to deliver concrete, long-term results by looking past market noise to identify and isolate the factors that matter most, and by developing ideas that stand up to rigorous testing. By putting theory into practice, we have become a leader in alternative strategies and an innovator in traditional portfolio management since 1998. At AQR India, our employees share a common spirit of academic excellence, intellectual honesty and an unwavering commitment to seeking the truth. We’re determined to know what makes financial markets tick – and we’ll ask every question and challenge every assumption. We recognize and respect the power of collaboration and believe transparency and openness to new ideas leads to innovation. The Team: AQR is building a new Data Engineering team within the Enterprise Engineering organization to focus on solving problems working with big data. This team will work directly with researchers and others across the firm to build and manage the infrastructure that loads, validates, and provisions many large data sets used in the research and investment processes. Your Role: As a data engineer you will participate in designing and building an new enterprise data platform used by quantitative researchers to manage large volumes of market, reference, and alternative data, both structured and unstructured. You will also build critical tools that will be used across the firm to validate data, back-test and find valuable investment insights. You will be collaborating directly with Researchers and Portfolio Managers to define and implement detailed business requirements for new and existing projects. This critical team will use big data technologies and cloud-based platforms to build an extensible and scalable solution to the data needs of one of the largest quantitative investment firms in the world. What You’ll Bring: 3+ Years of relevant work experience Bachelors/Masters/PhD in Computer Science, Engineering, or related discipline Outstanding coding, debugging, and analytical skills Ability to work directly with business stakeholders to spec out and implement solutions Experience working in a distributed data environment and working with large data sets Knowledge of big data frameworks such as Spark and Hadoop An interest in quantitative finance (no finance / trading experience required) Familiarity with one of the large cloud providers is a plus but not required Experience with Python / NumPy / pandas or similar quantitative stack is a plus Familiarity with AI models and/or frameworks is a plus Who You Are: Mature, thoughtful, and a natural fit for our collaborative culture Hard-working and eager to learn in a fast-paced, innovative environment Committed to intellectual integrity, transparency, and openness Motivated by the groundbreaking effects of technology-at-scale We believe that the next innovation to transform our business could come from anyone at AQR. Expect to be recognized not only for your diligence and hard work today, but for your vision for tomorrow. We are mathematicians, computer scientists, engineers and artists, passionate about advancing financial research and pushing the limits of today’s technology.

Responsibilities

As a data engineer, you will design and build an enterprise data platform for quantitative researchers. You will collaborate with researchers and portfolio managers to implement solutions for managing large volumes of data.