Data Analytics Engineer

***Position is bonus eligible***

Prestigious Financial Company is currently seeking a Data Analytics Engineer. Candidate will join a team supporting the design and implementation of cloud infrastructure for internal analytics zone in collaboration with the Data Platform team, data architects, DevOps, and IT.

Responsibilities:

Assist in the build, test, and deploy semantic layer’s virtual and physical data models that simplify complex semi-structured data, eliminate multiple definitions of similar data, create query-friendly datasets, and standardize column naming for downstream users that are developing quantitative analytics, dashboards, and internal risk applications.

Assist in maintaining performance and accuracy SLAs for semantic layer and other data products through observability practices, ensuring proactive detection of system failures and incident response

Learn user wants, motivations, priorities, and “the why” as part of eliciting business requirements with business users from various risk management department

Work with upstream data producers to understand how their systems work, how they generate data, and how that is subject to change over time to help manage schema drift

Collaborate with Data Governance, Data Platform Team, and DBAs to design access controls to data platform that meet business and internal governance need

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 our risk models that meet critical business user SLAs around latency and access patterns

Promote self-service capabilities and data literacy for business users leveraging the semantic layer, other analytics platforms (e.g. Tableau, python), and CI/CD tools

Invest in your continued learning of on data engineering best practices, cloud computing, options trading industry, and financial risk management, with an eye towards improving maintainability, reliability, and utility of our analytics infrastructure

Assist risk analysts in solving their analytics questions/challenges and support ad-hoc development with them, as needed.

Qualifications:

Ability to collaborate with multiple partners (e.g. 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 in mind.

Ability to communicate technical concepts to audiences with varying levels of technical background and synthesize non-technical requests into technical output

Comfortable supporting business analysts on high-priority projects

High attention to detail, tradeoffs, and an ability to think structurally about a solution

Technical Skills:

Ability to write and optimize complex analytical SQL queries

Ability to write and optimize python for custom data pipeline code (virtual environments, scripts vs. modules vs packages, functional programming, unit testing)

Experience with a source code version control repository system, branch management, pull requests (preferably Git)

Experience with data viz/prep tools (preferably Tableau and Alteryx)

[Preferred] Experience with transformation/semantic layer frameworks, such as dbt

[Preferred] Familiarity with services on at least one cloud computing platform, such as AWS or Azure, or a cloud data platform such as Databricks or Snowflake

[Preferred] Familiarity with data modeling design concepts such as 3rd-normal form or denormalization modeling concepts such as star-schema

[Preferred] Exposure to batch orchestration tools such as Apache Airflow, Dagster, or Prefect

[Preferred] Experience working with a linux shell and software containers for portable code distribution and execution, like docker

[Preferred] Experience with privileged access management platforms, such as CyberArk or Hashi Vault

[Preferred] Experience integrating custom code with CI/CD tools, such as Jenkins, JFrog Artifactory, Harness

[Preferred] Understanding of applied statistics and hands-on experience applying these concepts

Education and/or Experience:

Bachelor's or Master’s degree in a quantitative discipline (e.g., Statistics, Computer Science, Mathematics, Physics, Data Science, Electrical Engineering, Information Systems) or equivalent professional experience

3+ years of experience as a data engineer, software engineer, data scientist, financial risk analyst, business intelligence analyst

Certificates or Licenses:

[Preferred] Cloud platform certification, or

[Preferred] Data Engineering or BI tool certification, or

[Preferred] Financial Analyst certification