Data Analytics Engineer

Based in Chicago with a hybrid work arrangement, the Data Analytics Engineer role at Request Technology centers on building and maintaining semantic layer data models and ETL and data serving solutions that power internal analytics. You will collaborate closely with the Data Platform, governance, and DevOps teams to support cloud infrastructure for analytics and to foster data literacy and self-service across the organization. The position offers a salary range of USD 100,000 to 130,000 per year and requires a bachelor’s or master’s degree in a quantitative discipline with at least three years of relevant experience.

Responsibilities

• Build, test, and deploy semantic layer data models (virtual and physical) that simplify complex semi-structured data, reduce multiple definitions of similar data, create query-friendly datasets, and standardize column naming for downstream users developing quantitative analytics, dashboards, and internal risk applications.
• Maintain performance and accuracy SLAs for the semantic layer and other data products through observability practices, ensuring proactive detection of system failures and timely incident response.
• Learn user wants, motivations, priorities, and the why as part of eliciting business requirements with business users from various risk management departments.
• Collaborate with upstream data producers to understand how their systems generate data and how changes over time can affect schema drift, helping to manage it effectively.
• Work with Data Governance, Data Platform Team, and DBAs to design data access controls that meet business and internal governance needs.
• Create documentation and testing to ensure data lineage is traceable and semantic layer components are easily discoverable and useful to business users.
• Support the implementation of ETL and data serving solutions for large datasets generated by risk models, meeting latency and access-pattern SLAs critical to business users.
• Promote self-service capabilities and data literacy for business users leveraging the semantic layer, analytics platforms (such as Tableau and Python), and CI/CD tools.
• Invest in ongoing learning of data engineering best practices, cloud computing, the options trading industry, and financial risk management to improve maintainability, reliability, and utility of analytics infrastructure.
• Assist risk analysts in solving analytics questions and provide ad-hoc development support as needed.

Requirements

• Ability to collaborate with multiple partners including Business Users, Data and Solution Architects, Data Governance and IT teams (Data Platform Team, Systems & Infrastructure, Security, DevOps, Networking) to craft solutions that align business goals with internal processes, security, and delivery standards.
• Ability to communicate technical concepts to audiences with varying levels of technical background and translate non-technical requests into technical outputs.
• Comfortable supporting business analysts on high-priority projects.
• High attention to detail, the ability to evaluate tradeoffs, and the capacity to think structurally about a solution.

Technologies

• SQL
• Python
• Git
• Tableau
• Alteryx
• dbt
• AWS
• Azure
• Databricks
• Snowflake
• Linux shell
• Docker
• CyberArk
• Hashi Vault
• Jenkins
• JFrog Artifactory
• Harness
• Apache Airflow
• Dagster
• Prefect

Benefits

• 401(k)
• Dental insurance
• Health insurance
• Paid time off
• Vision insurance