Data Engineer II - Enterprise Analytics

The Venetian Las Vegas•Las Vegas, NV

4d•Onsite

About The Position

The primary responsibility of the Data Engineer II – Enterprise Analytics is assisting in designing, developing, and deploying data-driven solutions as part of Enterprise Analytics data strategy and goals. Data Engineer II – Enterprise Analytics is responsible for creating reliable ETLs and scalable data pipelines to support Analytics and BI environment (including modeling and machine learning, visualizations, reports, forecasts, etc.). Data Engineer II – Enterprise Analytics participates development of robust data models by interpreting business logic required to turn complex ideas into a sustainable value-add processes. All duties are to be performed in accordance with departmental and The Venetian Resort’s policies, practices, and procedures.

Requirements

21 years of age.
Proof of authorization/eligibility to work in the United States.
Bachelor’s degree in computer science, information systems, engineering, analytics, or related field is required; Master’s degree preferred.
Must be able to obtain and maintain a Nevada Gaming Control Board registration and any other certification or license, as required by law or policy.
3+ years of experience in building data pipelines and ETL processes is required.
3+ years of experience in writing advanced SQL, data mining and working with traditional relational databases (tables, views, window functions, scalar and aggregate functions, primary/foreign keys, indexes DML/DDL statements, joins and unions) and/or distributed systems (Hadoop, Big Query) is required.
1+ years of experience with programming/scripting languages such as Python or Big Query is required.
Excellent understanding of data types, data structures and database systems and their specific use cases is required.
Experience in Microsoft Azure, Google Cloud Platform, Databricks or other cloud-based development environments is required.
Ability to perform root cause analysis on external and internal processes and data to identify opportunities for improvement and answer questions.
Strong understanding of data modeling principles including Dimensional modelling, and Data Normalization principles is required.
Excellent critical thinker and effective problem solver with creative solutions.
Strong communication skills, especially for explaining technical concepts to nontechnical business leaders.

Nice To Haves

Hands-on experience using Git and working in a CI/CD development environment is preferred.
Strong understanding of performance tuning, especially in cloud-based environments, is preferred.

Responsibilities

Collaborate with Enterprise Analytics BI Analysts, Data Scientists, and other business stakeholders to understand business problems and data requirements to build data structures to be ingested by analytics products (e.g.: reports, dashboards, etc.) and complex algorithms that provide unique insights into data.
Build data pipelines that clean, transform, and aggregate data from disparate sources.
Develop robust data models, including dimensional models, that can be used to answer questions for overall business and assist Data Scientists in predictive models building.
Develop logic for KPIs as requested by the business leadership.
Troubleshoot existing and create new ETLs and pipelines, SSIS packages, DAGs, Python/ Big Query/ SQL stored procedures and jobs.
Write efficient and optimized SQL code for use in data pipelines and data processing.
Drive data quality processes like data profiling, data cleansing, etc.
Develop best practices and approaches to support continuous process automation for data ingestion and data pipelines.
Use innovative problem solving and critical thinking approaches to troubleshoot challenging data obstacles.
Test, optimize, troubleshoot, and fine-tune queries for maximum efficiency in addition to accuracy of results.
Perform QA and UAT processes to foster an agile development cycle.
Participate in informal reviews of design, code, QA and UAT artifacts, both for owned work and for the work of colleagues. Provide challenging and meaningful feedback when appropriate.
Create documentation on table design, mapping out steps and underlying logic within data marts to facilitate data adoption with minimum guidance from the Enterprise Analytics management.
Identify opportunities for improvement not just in owned work, but also other areas of the department.
Safety is an essential function of this job.
Consistent and regular attendance is an essential function of this job.
Performs other related duties as assigned.