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Open nowPosted 3 days ago

Data Engineer (DV Equities)

DV Trading75 open roles

Where
Hong Kong
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Your applicationOpen nowData Engineer (DV Equities)DV Trading · Hong Kong

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on DV Trading's own form.

The reply lands in your private mailbox

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The clock on this job

Early applications get read.

7.8% of postings close within 7 days. Measured by our own scanner across the market. DV Trading postings stay open a median of 18 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.4%3 days
  3. 7.8%7 days
  4. 14.3%14 days
  5. 33.7%30 days
This job: posted 3 days ago

DV Trading median: 18 days open

The posting

About Us:

Founded two decades ago and headquartered in Chicago, the DV Group of financial services firms has grown to more than 600 people across North America, Europe and Asia. Since becoming an independent proprietary trading firm in 2016, DV Trading has scaled rapidly using its own capital, strategies and risk management to provide liquidity to markets worldwide. DV Group affiliates today include two broker-dealers, a cryptocurrency market maker and a growing investment adviser. DV Equities is the group's Asia-Pacific equities and futures market-making business, trading cash equities, ETFs, index futures and related products on exchanges across the region.

Overview:

We are looking for a Data Engineer to build and maintain the data infrastructure that powers our trading, research, and risk systems. You will work closely with quantitative researchers, traders, and software engineers to ensure that large-scale market data is clean, reliable, and easily accessible for both real-time trading and historical analysis. This is a hands-on role for someone who enjoys solving data problems in a fast-paced, collaborative environment.

Responsibilities:

  • Design, build, and maintain scalable data pipelines for ingesting, processing, and storing large volumes of market and trading data
  • Clean, validate, and enrich raw data to ensure high quality and consistency for research and production use
  • Optimize data storage, query performance, and processing workflows to support low-latency and high-throughput requirements
  • Collaborate with quantitative researchers and traders to understand data needs and deliver efficient data solutions
  • Work with software engineers to integrate data pipelines into the firm's trading and research infrastructure
  • Monitor data quality and pipeline health, troubleshoot issues, and implement improvements
  • Document data flows, schemas, and processes to support team collaboration and knowledge sharing

Requirements:

  • Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field
  • Strong proficiency in Python and SQL for data processing and analysis
  • Experience with data pipeline tools and frameworks (e.g., Airflow, Kafka, Spark, or similar)
  • Familiarity with relational and non-relational databases, as well as data lake/warehouse technologies
  • Solid understanding of data modeling, ETL/ELT processes, and data quality best practices
  • Experience working with large-scale datasets and optimizing performance
  • Strong problem-solving skills and attention to detail
  • Ability to communicate clearly and collaborate effectively with both technical and non-technical teams
  • Experience in financial markets or trading data is a plus

DV is not accepting unsolicited resumes from search firms. Only search firms with valid, written agreements with DV should submit resumes in response to DV’s posted positions. All resumes submitted by search firms to DV via e-mail, the Internet, personal delivery, facsimile, or any other method without a valid written agreement shall be deemed the sole property of DV, and no fee will be paid in the event the candidate is hired by DV. DV is proud to be an equal opportunity employer and committed to creating an inclusive environment for all employees.

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