Data Engineer focused on analytics and data pipeline development for network optimisation. Collaborating with teams to deliver high - quality data solutions with Python and SQL.
About the role
- BI Data Engineer supporting analytics and decision-making for Kpler's products. Responsible for building scalable pipelines and robust data models in a dynamic market landscape.
Responsibilities
- Architect and develop scalable, resilient, high-quality data solutions to support analytics, reporting, and machine learning workloads.
- Design, implement, and maintain reliable ETL/ELT pipelines that integrate, cleanse, and consolidate data from diverse internal and external sources, including APIs and third-party systems.
- Ensure data is accurately ingested, transformed, stored, and made accessible through robust testing frameworks and clearly defined business rules.
- Translate business requirements into scalable data models and transformation logic, working with large, multidimensional datasets to surface insights, trends, and opportunities.
- Build and maintain the BI reporting codebase, ensuring high standards of data integrity, availability, and performance across the organization.
- Create and maintain backend infrastructure that supports analytics platforms, dashboards, and machine learning pipelines.
- Develop and execute unit and integration tests for data pipelines and transformation scripts to ensure reliability and consistency.
- Proactively identify inefficiencies and bottlenecks in data flows and propose scalable, forward-looking solutions.
- Partner closely with product managers, engineers, commercial teams, and business stakeholders to understand strategy and data needs, aligning delivery with the BI roadmap.
- Contribute to team growth through code reviews, design discussions, and knowledge sharing, while staying current with industry best practices.
- Take ownership of the quality, integrity, and consistency of the BI data codebase and associated datasets.
- Ensure ETL efficiency and data availability to meet stakeholder requirements and business SLAs.
- Adhere to engineering best practices, including documentation, version control, and maintainable code standards.
- Support and deliver against team OKRs and KPIs, contributing to overall team and business success.
Requirements
- 2-4 years of back-end and/or data engineering experience, delivering production-grade data solutions.
- Demonstrated experience working in a global or international environment, collaborating across regions and time zones.
- Significant experience working with Python.
- Experience with SQL and NoSQL databases for OLTP and OLAP usages.
- Experience ingesting and processing data from external APIs.
- Experience with GCS (Google Cloud Services) and modern data warehousing (ideally BigQuery).
- Familiarity with BI data architecture and version control tools such as GitHub or GitLab.
- Understanding of performance optimization.
- Strong understanding of the business logic behind datasets, with an ability to ensure trust and data quality.
- Analytical, detail-oriented, and committed to building reliable, scalable solutions.
- Comfortable working with unstructured or imperfect data and transforming it into actionable insights.
- A collaborative team player who thrives in a global, multicultural environment.
- Strong communicator who can translate complexity into clarity for non-technical stakeholders.
Benefits
- Fair, inclusive, and diverse work environment
- Opportunity for personal and professional growth
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