Data Analyst

DESCRIPTION

Key Responsibilities:

• Conduct descriptive and diagnostic analytics on data sources.
• Apply machine learning tools on data and metadata to improve data quality.
• Identify patterns and trends in data sets.
• Create reports and dashboards on analysis results using business intelligence technologies.
• Develop data profiles for data tables and elements in the data lake.
• Create data catalog entries and ensure data catalog metadata quality.
• Analyze diverse data sets to identify data quality, coherency, and integrability issues and reduce data redundancy.
• Collaborate to develop data cleansing methods and rules.
• Assist in the creation and maintenance of documentation of key decisions, rules, controls, and processes.

RESPONSIBILITIES

Qualifications:

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

Competencies:

• Action Oriented: Taking on new opportunities and tough challenges with a sense of urgency, high energy, and enthusiasm.
• 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.
• Tech Savvy: Anticipating and adopting innovations in business-building digital and technology applications.
• Data Analytics: Discovering, interpreting, and communicating qualitative and quantitative data; determining conclusions relying on knowledge of business or functional frameworks; simultaneously applying statistics, data validity, data visualization, and problem-solving approaches to effectively extract meaningful patterns and business insights; presenting conclusions and outcomes that enable data-driven business decisions.
• Data Communication and Visualization: Constructing a tale 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 supports effective information governance across operational business processes and decision-making.
• Values Differences: Recognizing the value that different perspectives and cultures bring to an organization.

QUALIFICATIONS

Skills:

• Demonstrated experience as a Data Analyst or in a related data-centric role.
• Proficiency in SQL (preferably SQL Server) for querying and manipulating data.
• Practical experience with Databricks for tasks related to data engineering, data science, and analytics.
• Experience working with Databricks Lakehouse architecture and data warehousing concepts.
• Knowledge of Azure cloud services, including Azure Data Factory and Azure SQL Database.
• Expertise in Python programming.
• Skilled in using PowerBI to create interactive reports, dashboards, and visualizations, with familiarity in SSAS models.
• Strong analytical mindset with excellent problem-solving skills and keen attention to detail.
• Good communication skills, capable of conveying complex data insights clearly to non-technical stakeholders.

Experience:

• Relevant experience preferred such as working in a temporary student employment, intern, co-op, or other extracurricular team activities.
• Knowledge of the latest technologies and trends in data science is highly preferred and includes:
• SQL query language execution on NoSQL and SQL sources.
• Exposure to Agile software development.
• Exposure to IoT technology.
• Data profiling tools, technologies, and coding.
• Data catalog tools and technologies.
• Business intelligence tools and technologies.
• Exposure to Big Data open source.
• Clustered compute cloud-based implementation experience.