About the role :

Own the data platform that ingests customer ERP, warehouse and transport data and turns it into clean, modelled datasets for analytics and ML forecasting.

Role Overview :

As a Senior Data Engineer, you will serve as the architectural backbone of our data ecosystem, transforming raw, disparate information into high-fidelity assets that drive strategic decision-making. You will work closely with cross-functional teams, including data scientists, product managers, and business stakeholders, to design and maintain scalable pipelines that power our core analytical products. By bridging the gap between complex infrastructure and actionable business intelligence, you will directly influence our ability to deliver personalized user experiences and optimize operational efficiency at scale.

Key Responsibilities :

- Architect and maintain robust data pipelines using Python and Apache Spark to ensure seamless ingestion and processing of high-volume datasets for downstream analytics.

- Orchestrate complex workflows using Airflow to guarantee data reliability and timely delivery for business-critical reporting.

- Develop and optimize modular data models using dbt to transform raw data into clean, business-ready schemas within Snowflake or BigQuery.

- Implement real-time data streaming solutions using Kafka to provide low-latency insights for time-sensitive business operations.

- Collaborate with engineering teams to establish best practices in data governance, security, and performance tuning, ensuring the long-term stability of our data platform.

Required Skillset :

- Demonstrated expertise in building high-performance data pipelines using Python and Apache Spark, with a deep understanding of distributed computing principles.

- Proven ability to design and manage complex data architectures in cloud-native environments like Snowflake or BigQuery, ensuring cost-efficiency and query optimization.

- Strong proficiency in SQL for advanced data manipulation and a clear understanding of modern data transformation workflows using dbt.

- Experience in designing event-driven architectures using Kafka to support real-time data requirements.

- Exceptional communication skills, with the ability to translate complex technical requirements into clear, actionable insights for non-technical stakeholders.

- A collaborative mindset, comfortable working in a fast-paced, hybrid environment where cross-team coordination is essential for project success.

- A degree in Computer Science, Engineering, or a related quantitative field, reflecting a strong foundation in algorithmic thinking and system design.