IncepIT

Overview of job

We are looking for Analytics Engineers who can transform raw and fragmented data into reliable, well-structured and reusable data products.

This position combines analytics, data engineering and data-quality responsibilities. You will use SQL, Python and AI-assisted tools to build dependable datasets, automate recurring work and make data easier for analysts and business teams to understand and use.

We value a strong data-organization mindset: clear definitions, consistent naming, documented ownership, traceable lineage, automated quality checks, version control and reproducible work.
 
Responsibilities:

• Understand business requirements and translate them into appropriate data models and technical solutions.
• Build and maintain clean, reusable analytical datasets and data models.
• Develop SQL and Python workflows for data transformation, validation, reconciliation and automation.
• Convert recurring manual processes into tested and reproducible workflows.
• Establish automated checks for data completeness, freshness, consistency and accuracy.
• Investigate data-quality issues and coordinate with relevant teams to resolve their root causes.
• Maintain data dictionaries, metric definitions, lineage documentation and naming standards.
• Apply version control, testing and code-review practices.
• Improve the accessibility and discoverability of trusted data.
• Support analysts and business users in using data correctly and consistently.
• Communicate technical decisions, limitations and trade-offs to technical and non-technical stakeholders.
• Use approved AI agents to accelerate data exploration, coding, testing and documentation.
• Independently review AI-generated work and protect confidential or personal information.

• At least three years of hands-on experience in analytics engineering, data engineering, advanced analytics or a closely related position.
• Strong SQL skills, including complex joins, aggregations, window functions and data-quality investigation.
• Practical Python experience for data processing, automation and validation.
• Experience organizing fragmented or inconsistent data into maintainable analytical datasets.
• Understanding of data modeling and ETL/ELT workflows.
• Experience implementing data-quality checks and troubleshooting data issues.
• Experience with Git or another version-control system.
• Strong documentation and problem-solving habits.
• Professional English communication skills.
• Ability to work independently while collaborating effectively with technical and business teams.
• Demonstrated ability to use AI-assisted tools responsibly and verify their output independently.