Data Engineer

HS Financial Technologies (HSFT) is a global technology and fund administration partner to alternative asset managers, supporting more than $1 trillion in assets under administration. We serve private equity, private credit, hedge funds, endowments, and others with integrated technology, specialized expertise, and scalable operations.

Our proprietary platform connects data, workflows, and operations across the investment lifecycle, delivering the automation, transparency, and insights clients need to operate efficiently and grow with confidence. Combined with deep expertise across fund accounting, middle office, investor services, regulatory reporting, tax, risk, and front-office solutions, we help clients navigate complexity at scale.

More than 2,400 professionals across 14 global offices deliver the expertise, responsiveness, and consistency that have made HSFT a trusted partner to alternative asset managers worldwide.

At HSFT, we are committed to helping employees build meaningful, long-term careers through structured development programs, leadership opportunities, and clear paths for growth. Guided by our core values of Relationships, Support, Innovation, and Expertise, we foster a collaborative environment where people are empowered to learn, contribute, and succeed.

We offer flexible working arrangements, comprehensive health and well-being benefits, and a culture that invests in our people because they are the foundation of our success.

Founded in 2008 as HedgeServ, HSFT is recognized as a leading provider of alternative asset services, earning industry recognition across technology, accounting, client service, investor services, and regulatory expertise.

Job Description:

The Data Engineer supports the Product and Portfolio Support Team in the development, deployment, and maintenance of enterprise-wide Extract-Transform-Load (ETL) processes. This role helps design, build, and support data pipelines that transform information from external vendors and clients into formats suitable for internal applications. 

The role also includes developing and maintaining Python-based solutions, including Jupyter notebooks, and supporting the migration of legacy processes into standardized, scalable code. 

The position contributes to the transition from legacy operations to modern data platforms and works closely with business, technical, and support teams to maintain critical ETL workflows. It also requires analysing and interpreting data to support operational and business needs. 

The role combines operational support with project-based work, including internal initiatives and cross-functional collaboration. Responsibilities include contributing to process documentation, automation solutions, and continuous improvement across data engineering practices. 

The position operates within a distributed global team and follows defined service level requirements, including coverage aligned with U.S. business hours. 

Job Responsibilities:

• Design, build, test, and maintain ETL pipelines using Python and Jupyter notebooks to support data processing requirements. 

• Transform raw data from external sources into structured formats suitable for internal systems. 

• Produce and maintain ETL documentation to ensure clarity, consistency, and auditability. 

• Provide support for incidents and service requests in line with defined service levels, including deploying fixes where required. 

• Contribute to continuous improvement by applying relevant tools, technologies, and best practices in data engineering. 

• Translate technical requirements and data issues into structured solutions for internal stakeholders. 

• Support process automation and the development of data extraction and transformation solutions across business units. 

• Contribute to internal standards, guidelines, and root cause analysis processes. 

• Keep up-to-date with industry best practices, tools, and emerging technologies relevant to ETL and data engineering, and apply them to the team’s workflows.

Job Requirements:

• Bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent practical experience. 

• Experience developing ETL processes or working with data pipelines. 

• Experience in environments involving data transformation, automation, or software development. 

• Proficiency in Python, including experience with Jupyter notebooks. 

• Experience with SQL and Bash scripting. 

• Knowledge of data processing libraries such as Pandas, NumPy, and SciPy. 

• Advanced Excel skills, including complex formula development. 

• Ability to interpret XML schemas and work with XML-based processes. 

• Experience with AWS technologies, including monitoring and troubleshooting tools such as Kibana and APM. 

• Understanding of software engineering principles, including object-oriented programming and design patterns. 

• Familiarity with agile development, release management, and production deployment processes. 

• Experience in the financial services industry and some knowledge of financial products is a plus.

• Excellent written and verbal communication skills in English.

• All qualifications are equally important; we are looking for a blend of technical expertise with a proactive, service‑oriented mindset.

Please note that this role requires working during U.S. business hours.