Senior Associate, Data Analytics Engineering

What You'll Do

This role will be a key contributor to the design and development of the semantic layer within the organization's data platform, specifically in the analytics environment. The individual will become a subject matter expert on core business data sets, enabling teams across the company to effectively leverage data for decision-making. The role involves addressing strategic data challenges, gathering business user requirements, designing and implementing shared analytics infrastructure, building proof-of-concepts, and evolving analytics platforms to meet the needs of developers while adhering to security and IT standards.

Primary Duties and Responsibilities

To perform this role successfully, the individual must be able to execute each primary duty effectively:

• Support the design and deployment of cloud infrastructure for the internal analytics environment in collaboration with data platform teams, data architects, DevOps, and IT.
• Assist in building, testing, and deploying virtual and physical data models within the semantic layer to simplify complex semi-structured data, eliminate redundant definitions, create query-friendly datasets, and standardize naming conventions for downstream users.
• Help maintain performance and accuracy SLAs for the semantic layer and other data products through observability practices, ensuring proactive issue detection and incident response.
• Elicit business requirements by understanding user motivations, priorities, and goals across various departments.
• Collaborate with upstream data producers to understand system functionality, data generation processes, and manage schema changes over time.
• Work with Data Governance, Platform Teams, and DBAs to design access controls that align with business needs and internal governance policies.
• Create documentation and testing protocols to ensure traceable data lineage and discoverability of semantic layer components.
• Support the implementation of ETL and data serving solutions for large datasets generated by risk models, meeting business SLAs for latency and access.
• Promote self-service analytics and data literacy among business users using tools such as Tableau, Python, and CI/CD platforms.
• Continuously invest in learning best practices in data engineering, cloud computing, financial risk management, and industry-specific knowledge to enhance infrastructure reliability and maintainability.
• Assist analysts with ad-hoc analytics challenges and support development efforts as needed.

Qualifications

The following qualifications are representative of the knowledge, skills, and abilities required. Reasonable accommodations may be made for individuals with disabilities.

• Required: Ability to collaborate across teams (e.g., Business Users, Architects, Governance, IT, DevOps, Security) to design solutions that align with business goals and internal standards.
• Required: Strong communication skills to explain technical concepts to varied audiences and translate non-technical requests into technical deliverables.
• Required: Comfort working with business analysts on high-priority initiatives.
• Required: High attention to detail and ability to think structurally about solutions.

Technical Skills

• Required: Proficiency in writing and optimizing complex SQL queries.
• Required: Experience with Python for custom data pipelines, including virtual environments, functional programming, and unit testing.
• Required: Familiarity with version control systems (preferably Git), including branch management and pull requests.
• Required: Experience with data visualization and preparation tools (preferably Tableau and Alteryx).
• Preferred: Experience with semantic layer frameworks such as dbt.
• Preferred: Familiarity with cloud platforms (e.g., AWS, Azure) or cloud data platforms (e.g., Databricks, Snowflake).
• Preferred: Understanding of data modeling concepts (e.g., 3rd-normal form, star-schema).
• Preferred: Exposure to orchestration tools like Apache Airflow, Dagster, or Prefect.
• Preferred: Experience with Linux shell and containerization tools like Docker.
• Preferred: Experience with privileged access management tools (e.g., CyberArk, Hashi Vault).
• Preferred: Experience integrating custom code with CI/CD tools (e.g., Jenkins, JFrog, Harness).
• Preferred: Applied statistics knowledge and hands-on experience.

Education and Experience

• Required: Bachelor's or Master's degree in a quantitative field (e.g., Statistics, Computer Science, Mathematics, Physics, Data Science, Engineering, Information Systems) or equivalent professional experience.
• Required: Minimum of 3 years of experience in roles such as data engineer, software engineer, data scientist, financial risk analyst, or business intelligence analyst.

Certifications (Preferred)

• Cloud platform certification
• Data Engineering or BI tool certification
• Financial Analyst certification