Enterprise Data Engineer

Boston, MA

Applications have closed

GMO

GMO partners with sophisticated institutions, financial intermediaries, and families to provide innovative solutions to meet their long-term investment needs.

View company page

Company Profile
Founded in 1977, GMO is a global investment manager committed to delivering superior long-term investment performance and advice to our clients. We offer strategies and solutions where we believe we are positioned to add the greatest value for our investors. These include multi-asset class, equity, fixed income, and alternative offerings.  We manage approximately $60bn for a client base that includes many of the world’s most sophisticated institutions, financial intermediaries, and private clients. Industry-wide, we are well known for our focus on valuation-based investing, willingness to take bold positions when conditions warrant, and candid and academically rigorous thought leadership. Jeremy Grantham, GMO’s Co-Founder and Long-Term Investment Strategist, is renowned as an expert in identifying speculative investment bubbles and also as a leading climate investor and advocate. GMO is privately owned and employs over 430 people worldwide. Headquartered in Boston, we also have offices in San Francisco, London, Amsterdam, Singapore, Sydney, and Tokyo (a representative office). Our company-wide culture emphasizes commitment to clients, intellectual curiosity, and open debate. We celebrate and respect our differences, while embracing and valuing what each of us brings to work, as we know that diverse teams in an inclusive, caring environment achieve higher engagement and better client results. 
Position Overview
The Enterprise Data Engineering Team is responsible for the development and support of GMO’s critical data processing and warehousing applications.  This team consists of data engineers whose key functions include database development, system integration, and production support. Engineers are responsible for implementation and maintenance of multiple proprietary databases, with a focus on an Azure Synapse Enterprise Data Warehouse.  The candidate will participate in an agile development lifecycle. The role offers the candidate an opportunity to innovate while working with seasoned engineers in support of GMO’s key business groups.

Primary Responsibilities:

  • Design and implement SQL and Python solutions for GMO’s Portfolio Management, Operations, Marketing, and Client Reporting teams
  • Maintain an Azure Synapse Enterprise Data Warehouse
  • Migrate existing systems to a cloud environment
  • Implement optimal data modeling and access patterns to allow for performant but cost-effective data use
  • Support the production processing of data within the Enterprise data ecosystem

Mandatory:

  • 2-5 years of SQL and Python programming experience
  • Experience and interest in working in the financial services industry
  • Experience with cloud-based platforms such as Microsoft Azure or AWS
  • Familiarity with distributed enterprise applications
  • Understanding of Agile practices
  • Undergraduate degree in Computer Science

Preferred:

  • Experience with Azure data platforms including Synapse, SQL Database, Data Factory and Data Lake
  • Experience working with data (files, rest APIs, databases) in Python
  • Experience migrating distributed applications to a cloud-based platform
  • Experience working with data warehousing technologies
  • Experience working with financial data systems
  • Familiarity with NeoXam DataHub helpful but not required

* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: Agile APIs AWS Azure Computer Science Data warehouse Data Warehousing Engineering Python SQL

Region: North America
Country: United States
Job stats:  9  0  0
Category: Engineering Jobs

More jobs like this

Explore more AI, ML, Data Science career opportunities

Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.