Senior Data Analytics Engineer

Noon Academy

2d•Hybrid

About The Position

We’re looking for a Senior Data Analytics Engineer to join our data & analytics team and help build the data foundations that power decision-making across the company. In this role, you’ll own the curated datasets, metric definitions, and data models that act as the company’s single source of truth. Your work will ensure that analysts, product managers, and leadership can trust the numbers they rely on every day. You’ll collaborate closely with engineers, analysts, product managers, and operations teams to improve how data is structured, validated, and accessed. A key part of the role is making our data systems scalable and reliable so teams can move faster with confidence. This position offers flexibility with a hybrid/fully remote work setup while still collaborating closely with cross-functional teams.

Requirements

Strong SQL, Python and data modelling skills: You’re highly proficient in SQL and comfortable using Python and designing datasets that are reliable, scalable, and easy for others to use.
Experience building reliable data systems: You’ve worked with production datasets or pipelines and understand how to monitor, validate, and maintain them (e.g., Python, Airflow, AWS).
Cross-functional mindset: You’re comfortable collaborating with product managers, engineers, analysts, and business stakeholders.
Strong problem-solving ability: You enjoy digging into messy data problems, figuring out why numbers don’t match, and understanding what each metric truly represents.
Clear communicator: You can explain data logic and metric definitions in ways that make sense to both technical and non-technical audiences.
Ownership and pragmatism: You exercise good judgment, balancing speed and accuracy while focusing on solutions that are both reliable and practical.

Nice To Haves

Experience working with AI-driven analytics tools or assistants.
Experience building pipelines that integrate internal data with AI systems.
Experience working with both frontend and backend events.

Responsibilities

Build the company’s single source of truth: Design and maintain curated datasets and data models that power reporting, dashboards, and analysis across the business.
Define and standardize key metrics: Work with stakeholders to ensure important business metrics are clearly defined, consistent, and trusted across teams.
Improve data reliability: Implement validation checks, monitoring, and processes that prevent data issues and quickly detect anomalies.
Lead cross-functional data projects: Take ownership of high-impact initiatives from problem definition through delivery, aligning with product, engineering, and operations teams along the way.
Investigate and resolve data issues: Diagnose discrepancies in metrics, identify root causes, and work with engineers and analysts to implement long-term fixes.
Enable better self-serve analytics: Improve documentation, dataset structure, and discoverability so teams can explore and answer their own questions confidently.
Support our AI data tools: Help ensure our internal AI data analyst produces accurate answers by grounding it in trusted datasets and consistent metric definitions.
Mentor and support the data team: Share best practices with analysts and help raise the overall quality and reliability of our analytics work.