Principal, Data Analytics Engineering

Summary

This role is responsible for expanding OCC’s analytics capabilities by developing business-focused data engineering solutions and enterprise data models for our most important data assets. In this role, you will lead the design build of our internal analytics cloud data lake’s semantic layer and the maintenance of the supporting ETL processes, with an emphasis on transformation and serving curated data that accelerates the ability of teams across the organization in successfully harnessing these data. You will collaborate with business users and technical teams across the organization to facilitate data-driven decision making by enabling exploration and analysis of historical and near real-time data access using cloud-based tools and technologies. Lastly, you will be responsible for gathering requirements and designing solutions that address the problem at hand, anticipating the yet-to-be-asked questions, and align to the company’s security and IT standards.

Responsibilities

• To perform this job successfully, an individual must be able to perform each primary duty satisfactorily.
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  Partner with relevant business and technology teams to design and implement new cloud-based semantic layer to accelerate and simplify the analytics development of downstream users that develop quantitative analytics, internal risk apps, and BI dashboards.
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  Lead business and technology SMEs to develop OCC’s business domain model, which will empower communication and reasoning across the firm about the meaning of our core, proprietary data assets and how they generate and relate to each other.
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  Drive the build, test, and deployment patterns of the 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
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  Establish and maintain trust in the semantic layer by shaping our DataOps practices of observability, monitoring, and automation of quality checks and ETL pipeline health for proactive system failure detection and incident response, before our users find out.
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  Maintain user query performance requirements of our semantic layer by optimizing storage and SQL object materialization patterns
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  Collaborate with Data Governance team and DBAs to design access controls around our analytics warehouse to meet Data Governance needs
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  Work with upstream data producers to understand how their systems generate data, and how they are subject to change over time to help anticipate and manage schema drift from semantic layer
  Business Sensitive
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  Curate and maintain the company’s business domain model documentation
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  Create documentation to ensure data lineage is traceable and semantic layer components are easily discoverable and useful to business users from the data governance tools.
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  Invest in your continued learning of on modern data engineering, cloud computing, options trading industry, and financial risk management, with an eye towards improving maintainability, reliability, and utility of our analytics infrastructure

Supervisory Responsibilities

None

Are the essential duties of this job required to be performed in an OCC office location? No

Qualifications & Experience

• The requirements listed are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the primary functions.
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  [Required] 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.
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  [Required] Past experiences building data products, data warehouses, databases, and other analytics systems depended on by other employees or customers
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  [Required] Ability to communicate technical concepts to audiences and ask pressing questions of people with varying levels of technical background, and synthesize non-technical requests into a technical specification
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  [Required] High attention to detail, tradeoffs, and an ability to think structurally about a solution
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  [Required] Strong presentation and communication skills, including ability to clearly explain deliverables/results to non-technical audiences
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  [Required] Experience working within an agile environment
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  [Preferred] Subject matter experience working in the financial trading industry or a similar domain

Technical Skills & Background

Demonstrated proficiency in:

[Required] Experience implementing and maintaining cloud-based data warehouses or data lakes and maintaining a semantic layer that meets the needs of business users

•[Preferred] Familiarity with data modeling design concepts such as 3rd-normal form versus denormalized data modeling concepts like star-schema or data vault
•[Required] Ability to write and optimize complex analytical (SELECT) SQL queries and DDL queries, with an understanding of SQL optimization strategies
•[Required] Strong presentation and communication skills, including ability to clearly explain deliverables/results to non-technical audiences
•[Required] Ability to write and manage python projects for custom data pipeline code (virtual environments, functional programming vs. OOP, unit testing)
•[Required] Experience with a source code version control repository system, branch management patterns, pull requests, with a tool such as git and Github
•[Required] Experience working with a linux shell and with container technology, such as docker or podman for portable code distribution and execution
•[Preferred] Experience with transformation or semantic layer frameworks, such as dbt or Cube
•[Preferred] Experience with batch orchestration tools such as Apache Airflow
•[Preferred] Experience with data lake architectures, their open technology components and purpose in modern cloud data stacks for analytics, such as Apache Parquet, Apache Iceberg, and distributed or columnar optimized query engines, such as Trino or duckdb.
•[Preferred] Experience with PAM platforms, such as CyberArk or Hashi Vault
•[Preferred] Experience integrating code with CI/CD tools, like Jenkins, JFrog Artifactory
•[Preferred] Understanding of applied statistics and hands-on experience applying these concepts
•[Preferred] Experience with visualization/reporting tools, such as Tableau or Power BI
•[Preferred] Experience with self-service ETL tools, such as AWS Glue or Alteryx

Education & OR Experience

• [Required] Bachelor's degree in quantitative discipline (e.g., Statistics, Computer Science, Mathematics, Physics, Electrical Engineering, Industrial Engineering) or equivalent professional experience
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  [Preferred] Master's degree
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  [Required] 10+ years of experience as a data engineer, data modeler, or data scientist
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  [Preferred] Work experience in the global financial derivatives trading industry