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Senior Data Engineer (Manager Level)

lseg · GBR-Edinburgh-5th Floor

New
Senior 🇬🇧 English
Python Java Scala Databricks SQL Apache Spark Apache Kafka Presto Azure AWS GCP Power BI Oracle PostgreSQL SQL Server

Job description

About the role

LSEG (London Stock Exchange Group) is looking for a highly skilled Senior Data Engineer to design, build and manage its data infrastructure and analytics solutions. The role supports data-driven decision-making across the organization, working on complex datasets with cross-functional teams. The career stage listed is Manager.

Key responsibilities

  • Design, develop and optimize big data pipelines, architectures and data integration workflows.
  • Build scalable data storage solutions such as data lakes, warehouses or cloud-based systems.
  • Collaborate with data scientists, analysts and stakeholders to understand data requirements and deliver solutions.
  • Develop and maintain ETL processes for ingesting, transforming and loading data from diverse sources.
  • Conduct advanced data analysis, modelling and visualization to identify patterns and insights.
  • Ensure data quality, reliability and security across systems.
  • Use distributed computing frameworks such as Spark, Hive and Hadoop to process large datasets.
  • Stay up to date with trends and technologies in big data, analytics and cloud platforms.

Required profile

  • 10+ years of meaningful experience in big data and analytics.
  • Bachelor's or master's degree or equivalent experience in computer science engineering or a related field.
  • Strong programming skills in Python, Java or Scala.
  • Hands-on experience with Databricks, SQL and data modelling.
  • Excellent problem-solving and interpersonal skills.

Required skills

  • Big data tools such as Apache Spark, Apache Kafka and Presto.
  • Cloud services such as Azure, AWS and GCP.
  • Data visualization and business intelligence tools such as Power BI.
  • ETL workflows in databases and warehouses such as Oracle, PostgreSQL and SQL Server.
  • Preferred: machine learning frameworks (Azure ML Studio, AWS SageMaker), real-time stream analytics (Kafka, Azure Event Hubs) and real-time analytical databases (ClickHouse, Apache Druid/Pinot).

Questions fréquentes

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Source : ats:workday

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Published 1 day ago

Expires 1 month from now

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lseg

GBR-Edinburgh-5th Floor