Analytics Analyst - Quality

Key Responsibilities:

• Support data, compliance, and environment governance processes for the assigned domain.
• Facilitate an intake process for the assigned domain to reduce rework and promote reuse of analytics solutions.
• Collaborate with peers to integrate data from warehouses, data lakes, and other source systems to build relevant models for business use.
• Cleanse data to ensure report accuracy and reduce data duplication, maintaining documentation of the cleansing process.
• Support analytics projects to produce insights for the business to improve decision-making, delivering results using reports, business intelligence technology, or other appropriate mechanisms.
• Assist the business in establishing and enforcing guidelines and standards for data collection, integration, and processes.
• Support project work to ingest key data for the assigned domain into the data lake, ensuring the creation and maintenance of relevant metadata and data profiles.
• Assist stakeholders and business teams with finding necessary and relevant data when available.
• Participate in relevant communities of practice promoting the responsible use of analytics.
• Coach and assist business users in developing the capability to conduct work in the analytics ecosystem.
• Assist with the preparation of communications to leaders and stakeholders.

Technical Skills:

• Proficiency in Power BI for data visualization.
• Experience with the Databricks platform for data analytics and modeling.
• Thorough understanding of SSAS modeling (preferably tabular models).
• Strong knowledge of SQL for data querying and analysis.
• Strong hands-on experience with Python programming language.
• Understanding of applied statistics for data science.
• Experience with VBA for automating tasks.

Soft Skills:

• Analytical mindset with a focus on problem-solving.
• Good communication and teamwork skills.
• Ability to work under supervision and take initiative when needed.

• Minimal relevant work experience required.

• College, university, or equivalent degree in a relevant technical discipline, or relevant equivalent experience required.
• This position may require licensing for compliance with export controls or sanctions regulations.

• Collaborates : Building partnerships and working collaboratively with others to meet shared objectives.
• Communicates effectively : Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences.
• Customer focus : Building strong customer relationships and delivering customer-centric solutions.
• Interpersonal savvy : Relating openly and comfortably with diverse groups of people.
• Data Analytics : Discovering, interpreting, and communicating qualitative and quantitative data; determining conclusions relying on knowledge of business or functional frameworks; applying statistics, data validity, data visualization, and problem-solving approaches to extract meaningful patterns and business insights; presenting conclusions and outcomes that enable data-driven business decisions.
• Data Mining : Extracting insights from data by identifying relationships and patterns through data exploration and visualization techniques to understand the underlying structure of the data and enable sound conclusions upon model building.
• Data Modeling : Creating, writing, and testing data models, test scripts, and build scripts using industry standards and tools, version control, and build and test automation to meet business, technical, security, governance, and compliance requirements.
• Data Communication and Visualization : Constructing a narrative of the business problem, root cause, solution options, and opportunities through illustrating data visually, including reports and dashboards.
• Data Literacy : Expressing data in context, including data sources and constructs, analytical methods, and applied techniques; describing the use-case application and resulting value.
• Data Profiling : Assessing data issues and cleansing requirements to perform data extraction, mapping, collection, and testing; establishing good, quality data.
• Data Quality : Identifying, understanding, and correcting flaws in data that support effective information governance across operational business processes and decision-making.
• Values differences : Recognizing the value that different perspectives and cultures bring to an organization.