Analytics Engineer (Temporary Maternity Leave Replacement)

Team8

Team8

Posted on Apr 28, 2026

Description

About the Position

At Harmonya, we’re building cutting-edge AI solutions powered by high-quality, reliable data. As an Analytics Engineer within the Delivery & Operations organization, you will operate at the intersection of data engineering and analytics, with a strong focus on data quality, reliability, and scalability.

This role combines hands-on ownership of data pipelines and data transformations with the ability to effectively interface with customer-facing teams (Customer Success, and Delivery) when needed—helping ensure that data outputs are accurate, clear, and aligned with real-world use cases.

Responsibilities

  • Build and maintain data models, pipelines, and ETL processes to support analytics, reporting, and machine learning.
  • Own data quality and validation, including monitoring, auditing datasets, and identifying anomalies.
  • Support customer-facing teams by providing reliable data, clarifying definitions, and investigating data issues.
  • Collaborate cross-functionally and work with existing codebases to debug, improve, and maintain data workflows.
  • Ensure high-quality data across the lifecycle to support reliable ML pipelines.

Requirements:

Requirements

  • 3+ years of experience in Python development (production-level data logic, not just scripting)
  • 2+ years of experience with data validation / data quality practices
  • Experience with data pipelines / ETL processes
  • Proficiency in Pandas (or similar libraries)
  • Strong SQL and database knowledge
  • Experience working with existing production codebases (debugging, refactoring)
  • Ability to communicate clearly with non-technical stakeholders when needed
  • Strong analytical thinking and problem-solving skills
  • High attention to detail and commitment to data accuracy

Advantages

  • Familiarity with data modeling best practices
  • Experience supporting customer-facing data use cases or deliverables
  • Background in DataOps / data reliability practices
  • Exposure to machine learning pipelines