Senior Data Engineer

As a Senior Data Engineer, you will play a pivotal role in designing, building, and maintaining a robust data platform to empower our customers with critical data insights. You will collaborate closely with Product and Engineering teams to develop a secure, scalable, and efficient data ecosystem that enables users to make informed investment decisions. You will be tasked with creating innovative data pipelines, integrating multiple data sources, and driving data quality and automation to support the company's next phase of growth.

Mandatory Requirements:
5+ years of experience as a Data Engineer.
Expertise in Star/Snowflake schema design, data modeling, data pipelining, and MLOps.
Proficient in Data Warehouse technologies (e.g., Snowflake, AWS Redshift).
Strong experience with AWS data pipelines (Lambda, AWS Glue, Step Functions).
Proficiency in SQL and at least one major programming language such as Python or Java.
Familiarity with Data Analysis Tools like Looker or Tableau.
Experience with data tools like Pandas, Numpy, Scikit-learn, and Jupyter notebooks.
Knowledge of Git, GitHub, and JIRA.
Fintech or Financial Services industry experience.
Strong attention to detail and proactive in identifying and resolving data quality issues.
Ability to simplify, automate tasks, and build reusable components.
End-to-end data platform support experience.
Proven ability to collaborate with analysts, business, and engineering stakeholders.
Independent and capable of prioritizing tasks while breaking down ambiguous problems into actionable steps.
Familiarity with agile methodologies.
Nice to Have:
Knowledge of or eagerness to learn about the Private Equity/Venture Capital ecosystem and associated secondary markets.

Responsibilities:
Design, develop, and maintain a secure, scalable, and efficient data platform that serves diverse use cases.
Build and maintain adaptable data pipelines to process large volumes of data from multiple sources, such as databases, APIs, SaaS, files, and event logs.
Standardize datasets to provide accurate and reliable data for various use cases.
Collaborate with Product and Engineering teams to ensure product changes integrate smoothly with the data platform.
Engage with diverse stakeholder teams to understand their challenges and provide tailored data solutions that align with their objectives.
Perform and automate data quality control to ensure high-quality data across sources.
Continuously seek opportunities to simplify and automate tasks, developing reusable components to improve efficiency.
Develop deep subject-matter expertise in the companys data systems, processes, and tables.