Senior Data Analyst to work on service innovation, program review and digital transformation for a public sector client (JP00000890)

Senior Data Analyst to work on service innovation, program review and digital transformation for a public sector client (JP00000890)

**Remote MST hours**

Must haves:

• Yes/No - Bachelor degree in Computer Science, IT or related field of study.
• 5+ years of Experience as a Data Engineer and/or Data Analyst.
• 3+ years of Experience designing efficient dimensional models (star and snowflake schemas) for warehousing and analytics.
• 3+ years of Experience developing and maintaining reports, dashboards, and visualizations using Power BI, DAX, Tableau, or Python libraries.
• 3+ years of Experience ensuring data quality, security, and governance.
• 5+ years of Experience manipulating and extracting data from diverse on-premises and cloud-based sources.
• 2+ years of Experience performing migrations across on-premises, cloud, and cross-database environments.
• 2+ years of Experience using Git, collaborative workflows, CI/CD pipelines, containerization (Docker/Kubernetes), and Infrastructure as Code (Terraform, ARM, CloudFormation) to deploy and migrate data solutions.
• 3+ years of Experience with SSIS, Azure Data Factory (ADF), and using APIs for extracting and integrating data across multiple platforms and applications.

A combination of the following experience is also required:

• 2+ years of Experience in advanced analytics (e.g., statistical modeling, predictive analytics, data mining).
• 1+ year of Experience in a public sector environment or an environment of equivalent size and complexity.
• 2+ years of Experience with Azure Databricks
• 2+ years of Experience with object-oriented and functional programming/scripting languages.
• 1+ year of Experience working with AI (e.g., machine learning, NLP)

Responsibilities:

Services and project deliverables should evolve as the work progresses, in response to emerging user and business needs, as well as design and technical opportunities. However, the following must be delivered (iteratively) over the course of the project:

Data Engineering:

- Design, develop, and maintain scalable data pipelines to ingest, transform, and store large volumes of data both on-premises (SSIS etc.) and in the cloud (Azure, AWS, GCP)

- Ensure data integrity, accuracy, and availability for analytical and operational use.

- Collaborate with software engineers, data scientists, and business stakeholders to understand data requirements and implement solutions.

- Optimize and improve existing data workflows and ETL processes.

- Implement data governance and security best practices.

- Manage data storage solutions, such as data lakes and data warehouses.

- A successful history of manipulating, processing and extracting value from large, disconnected datasets.

- Deploy data solutions in the cloud and with database tools (SQL, NoSQL).

Data Analytics:

- Perform exploratory data analysis to identify trends, patterns, and insights.

- Develop and maintain reports, dashboards, and visualizations using BI tools (e.g., Power BI, SaaS VA).

- Conduct statistical analysis and modeling to support business strategies.

- Communicate findings and recommendations to stakeholders in a clear and concise manner.

- Use SQL and other query languages to extract and manipulate data from various sources.

- Create plans and strategies to integrate data models and improve service delivery.

- Develop and share service metrics and performance dashboards.

- Facilitate the frequent delivery of services and deliverables to ensure the successful creation of the target state while mentoring Agile Teams to grow their analytics fluency.

- Support corporate priorities with data-driven evidence using analytics, visualizations, and data modeling.