Analytics Engineer

Role Overview

payabl. empowers businesses to grow through payments innovation and banking services. We are looking for a skilled Analytics Engineer to join our data team and bridge the gap between data engineering and data analytics. This role focuses on ensuring data quality, performing sanity and validation checks, and transforming raw data into clean, reliable datasets for analysis. The ideal candidate will have a strong understanding of business logic and data workflows to deliver high-quality data for analysts and stakeholders.

What you will do

• Design, develop, and maintain data models and transformations (dbt) turning raw lakehouse data into clean, validated datasets across bronze/silver/gold layers.
• Develop, implement, and maintain automated data quality checks, tests, and validation processes across data pipelines.
• Collaborate with data engineers, domain team leads, and analysts to translate business requirements into well-defined data models and metrics.
• Perform sanity and reconciliation checks to identify and resolve data inconsistencies, duplicates, and missing values.
• Contribute to the semantic layer: define and maintain consistent, well-documented metrics and dimensions used across BI and reporting.
• Document data models, quality issues, root causes, and resolutions to improve governance and discoverability.
• Provide analysts with reliable, well-prepared datasets to reduce direct engagement with raw data.
• Contribute to design and improvement of data workflows, orchestration, and automation around data quality and transformations.

What we need

• Bachelor’s degree in Computer Science, Data Science, Engineering, or related field or equivalent practical experience.
• Strong SQL skills and hands-on experience with data transformation tools (dbt strongly preferred).
• Proficiency in Python, including pandas for data analysis and validation.
• Experience with ELT/ETL pipeline design, implementation, and maintenance, including orchestration tools (Airflow).
• Familiarity with data lakehouse/warehouse technologies (e.g., Apache Iceberg, Trino, Apache Druid, Snowflake, Redshift).
• Experience with data quality and testing frameworks (dbt tests, Great Expectations, Soda, or similar).
• Proficiency with Git for version control and collaborative development workflows (merge requests, code review, CI/CD).
• Strong understanding of data modeling concepts (dimensional modeling, star schema) and data quality best practices.
• Experience working in cross-functional teams and communicating between technical and business stakeholders.
• Problem-solving mindset and attention to detail.

Nice to Have: Prior experience bridging data engineering and analytics; familiarity with financial data domains (online payments, banking, reporting); experience with BI tools (Apache Superset or similar) and semantic layer concepts; exposure to streaming/CDC technologies (Kafka, Debezium).

Benefits and Perks

Perks are location-specific (Cyprus office) and include Provident Fund, annual learning budget, monthly Wolt allowance, gym membership, potential company car after a year, parking, ample vacation and sick days, local discounts, language classes, and global events. Benefits and contract type may vary by location and will be discussed during the interview.

Hiring Process

• Initial Interview (30–40 minutes) with Talent Acquisition.
• In-Depth Discussion (60 minutes) with Hiring Manager.
• Technical Assessment (role-specific).
• Meet Our Leadership (60 minutes) with Chief Product Officer and CTO.

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