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Tesco

Posted 5 Oct 2026

Data Platform Engineer

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Employment
Full time
Team
Software Engineering
Posted
5 Oct 2026

About the role

We are looking for a Data Engineer/Data Platform Engineer with 3-8 years of experience to design, build, and operate scalable data platforms and pipelines. The ideal candidate will have strong expertise in SQL, Python/Java/Go, data processing frameworks, workflow orchestration, cloud platforms, and batch/streaming systems. You will work closely with engineering, product, and analytics teams to build reliable data infrastructure, optimize data workflows, and enable high-quality data for analytics and product use cases.


Responsibilities:

  • Build and maintain scalable ETL/ELT data pipelines for structured and unstructured data.
  • Design and optimize batch and real-time streaming pipelines using Spark, Kafka, Flink, etc.
  • Develop and maintain data models for analytics and product use cases.
  • Write clean, scalable, production-grade code using Python, Java, Go, or Ruby.
  • Build internal data services, integrations, automation, and developer tools.
  • Ensure data quality, integrity, availability, reliability, and performance.
  • Implement monitoring, logging, alerting, and automated testing for data pipelines.
  • Troubleshoot pipeline failures and optimize performance, scalability, and cost.
  • Collaborate with the Product, Analytics, and Engineering teams on data requirements.
  • Participate in system design and architecture discussions.
  • Follow best practices around data governance, security, and compliance.


Requirements:

  • 5.5-8.5 years of experience in data platform or data engineering roles.
  • Strong proficiency in SQL and experience with relational and NoSQL databases.
  • Hands-on experience with Airflow, DBT, Spark, or Flink.
  • Experience with streaming or batch data processing systems.
  • Proficiency in at least one programming language (Python, Go, or Java).
  • Familiarity with cloud platforms (AWS, GCP, or Azure).


Nice to Have:

  • Experience with real-time streaming (Kafka, Kinesis, Pub/Sub).
  • Exposure to data warehouses (Snowflake, BigQuery, Redshift).
  • Experience operating data platforms in high-scale or fintech domains.
  • Understanding of data security, privacy, and compliance.

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