Data Solutions Analyst and Engineering Support

Data Solutions Analyst and Engineering Support High-Level Position Purpose

Leverages strong analytical capabilities and growing engineering instincts to develop reliable, well-structured data solutions across the CX organization. The role is anchored in analysis, reporting enablement, stakeholder management, and problem solving, while progressively contributing to stronger data foundations, Python-based automation, SQL development, lightweight application building, and sustainable technical practices. Ideal for a Data Analyst with aptitude, curiosity, and fundamental experience in data engineering, AI-assisted development, and modern delivery tools, and an appetite to grow toward more advanced data engineering responsibilities over time.

Prepare, transform, and maintain datasets for reporting, automation, visualization and operational decision making.

Work closely with Customer Experience and other functions to observe, understand and map current processes and use cases and translate business requirements into technical requirements and solution designs.

Use SQL and Power BI to answer business questions, validate logic, and turn findings into clear recommendations.

Build and support Python scripts and automations that minimize manual effort in analytical workflows.

Work with experienced data engineers to understand pipelines, data sourcing, and database structures, and identify and implement improvements.

Use Git Hub and Confluence to document solutions, track versions (version control), and deliver solutions which include data definitions, process notes, and user instructions.

Use Git Hub Copilot and similar AI tools to improve the quality and speed of coding, testing, and documentation while ensuring full ownership and responsibility for the final product. Maintain the ‘Human in the Loop’.

Support pipeline, PostgreSQL, API, or cloud tasks with guidance from senior engineers.

Communicate progress, risks, and findings clearly to support delivery and stakeholder confidence.

• At least 2 years of experience supporting data‑driven tools and engineering‑oriented solutions, including backend services, lightweight web applications, and workflow automations.
• Bachelor’s degree or equivalent experience in computer science, information systems, data engineering, or related technical field.
• Strong analytical foundation. Ability to interpret data, spot patterns, explain drivers, and translate them into business recommendations.
• Excellent communication and stakeholder management skills. Able to work independently and liaise with peers and leaders for support
• Strong SQL, joins, aggregations, validation, and troubleshooting against relational datasets.
• Working in Python for data manipulation, scripting, and automation.
• Power BI, Excel, Apache Superset or similar for building and explaining reporting outputs.
• Comfort with Git Hub or similar version control tools, branching, pull requests, and code review.
• Ability to provide clear documentation of logic, assumptions, and steps in Confluence or equivalent.
• Familiarity with Git Hub Copilot, etc. with judgment to review and validate generated output.

Deeper PostgreSQL, structure, query optimization, views, functions, schemas, or data modeling.

Exposure to data pipelines, APIs, scheduled jobs, or integrations.

Experience with cloud platforms, ideally Azure (storage, compute, deployment, cost awareness).

AI tools beyond code completion, for testing, refactoring, debugging, or solution design.

Curiosity and aptitude to grow from analyst to more complex engineering work.

Ability to learn new platforms quickly. Can work through ambiguity and builds with increasing levels of independence.

Accuracy and timeliness of analytical outputs and datasets.

Reduction of manual effort through Python, SQL, and automation (ROI).

Clear documentation and version control that improves sustainability & reduces effort on maintenance.

Stakeholder confidence in data quality and findings (NPS).

Increased growth in engineering knowledge and complexity - adjacent delivery: databases, pipelines, cloud, and…