Data Analytics Engineer

We are looking for a seasoned Data Analytics Engineer with a strong analytical mindset and proven experience in transforming complex data into actionable insights. The ideal candidate has at least 5 years of hands-on experience working with data and thrives in dynamic, cross-functional environments. You will work closely with engineering, product, and business stakeholders to support data-driven decision-making across the organization, acting as an independent owner of data integrity and a key driver of data infrastructure growth.

Key Responsibilities

• Develop and manage datasets in a modern data warehouse environment (we use BigQuery).

• Design, build, and maintain robust data pipelines using dbt.

• Write, optimize, and maintain complex SQL queries for various analytical needs.

• Take independent ownership of data quality and data integrity across the data warehouse, proactively identifying inconsistencies, monitoring data reliability, and driving improvements at the source.

• Collaborate with engineers and other internal stakeholders to ensure data accuracy, accessibility, and relevance.

• Analyze and interpret complex datasets to support strategic business initiatives.

• Perform ad hoc and deep-dive analyses to uncover trends, anomalies, and key insights.

• Build interactive and insightful data visualizations and dashboards using Looker Studio, Power BI, or similar tools.

• Act as a data advocate within the organization by promoting best practices in data usage, quality awareness, as well as in building and using reports and dashboards, helping teams better understand and trust data.

• Document analytical processes and methodologies to ensure reproducibility and clarity.

Key Requirements

• A minimum of 5 years’ hands-on experience in a Data Analyst or related analytics role, ideally within SaaS or ecommerce environments.

• Solid background in using modern data warehouse platforms, such as BigQuery, Redshift, or Snowflake, including querying, performance tuning, and data modeling.

• Practical experience building and maintaining data transformation pipelines using dbt or similar tools (e.g. Dataform).

• Ability to proactively and independently drive data integrity and infrastructure growth, by identifying complex data inconsistencies and autonomously leading cross-functional dialogues with technical and business stakeholders—including Engineering, DevOps, Account Managers, and Sales—to expand data warehouse capabilities and ensure the accuracy and relevance of data sources.

• Proven ability to translate complex data insights into clear business actions, aligning analytical work with broader company objectives.

• Strong communication skills to coordinate with internal stakeholders and support improvements to input data at the source.

• Proficient in visual analytics with tools like Power BI and Looker Studio, capable of building impactful dashboards and reports.

• Strong enthusiasm for using data to inform decisions and optimize business outcomes.

• Familiarity with agile workflows, particularly Kanban, and experience contributing to iterative, fast-paced team environments.

• Strong command of English, both written and spoken.