open-source ETL, and data pipeline orchestration tools such as Apache Airflow and Nifi. Experience with large scale/Big Data technologies, such as Hadoop, Spark, Hive, Impala, PrestoDb, Kafka. Experience with workflow orchestration tools like Apache Airflow. Experience with containerisation using Docker and deployment on Kubernetes. Experience with More ❯
Solid understanding of ETL processes , data modeling, and data warehousing. Familiarity with SQL and relational databases. Knowledge of big data technologies , such as Spark, Hadoop, or Kafka, is a plus. Strong problem-solving skills and the ability to work in a collaborative team environment. Excellent verbal and written communication More ❯
S3, BigQuery, Redshift, Data Lakes). Expertise in SQL for querying large datasets and optimizing performance. Experience working with big data technologies such as Hadoop, Apache Spark, and other distributed computing frameworks. Solid understanding of machine learning algorithms, data preprocessing, model tuning, and evaluation. Experience in working with LLM More ❯
AWS Certified Data Engineer, or AWS Certified Data Analytics, or AWS Certified Solutions Architect Experience with big data tools and technologies like Apache Spark, Hadoop, and Kafka Knowledge of CI/CD pipelines and automation tools such as Jenkins or GitLab CI About Adastra For more than 25 years More ❯
Data Analytics - Specialty or AWS Certified Solutions Architect - Associate. Experience with Airflow for workflow orchestration. Exposure to big data frameworks such as Apache Spark, Hadoop, or Presto. Hands-on experience with machine learning pipelines and AI/ML data engineering on AWS. Benefits: Competitive salary and performance-based bonus More ❯
roles, with 5+ years in leadership positions. Expertise in modern data platforms (e.g., Azure, AWS, Google Cloud) and big data technologies (e.g., Spark, Kafka, Hadoop). Strong knowledge of data governance frameworks, regulatory compliance (e.g., GDPR, CCPA), and data security best practices. Proven experience in enterprise-level architecture design More ❯
Java Commercial experience in client-facing projects is a plus, especially within multi-disciplinary teams Deep knowledge of database technologies: Distributed systems (e.g., Spark, Hadoop, EMR) RDBMS (e.g., SQL Server, Oracle, PostgreSQL, MySQL) NoSQL (e.g., MongoDB, Cassandra, DynamoDB, Neo4j) Solid understanding of software engineering best practices - code reviews, testing More ❯
Frameworks: Extensive experience with AI frameworks and libraries, including TensorFlow, PyTorch, or similar. ? Data Processing: Expertise in big data technologies such as Apache Spark, Hadoop, and experience with data pipeline tools like Apache Airflow. ? Cloud Platforms: Strong experience with cloud services, particularly AWS, Azure, or Google Cloud Platform, including More ❯
large-scale data. Experience with ETL processes for data ingestion and processing. Proficiency in Python and SQL. Experience with big data technologies like ApacheHadoop and Apache Spark. Familiarity with real-time data processing frameworks such as Apache Kafka or Flink. MLOps & Deployment: Experience deploying and maintaining large-scale More ❯
Proven experience as a Data Engineer with a strong background in data pipelines. Proficiency in Python, Java, or Scala, and big data technologies (e.g., Hadoop, Spark, Kafka). Experience with Databricks, Azure AI Services, and cloud platforms (AWS, Google Cloud, Azure). Solid understanding of SQL and NoSQL databases. More ❯
years experience working on mission critical data pipelines and ETL systems, hands-on experience with big data technology, systems and tools such as AWS, Hadoop, Hive, and Snowflake Detailed problem-solving approach, coupled with a strong sense of ownership and drive A passionate bias to action and passion for More ❯
MongoDB, Cassandra). • In-depth knowledge of data warehousing concepts and tools (e.g., Redshift, Snowflake, Google BigQuery). • Experience with big data platforms (e.g., Hadoop, Spark, Kafka). • Familiarity with cloud-based data platforms and services (e.g., AWS, Azure, Google Cloud). • Expertise in ETL tools and processes (e.g. More ❯
East London, London, United Kingdom Hybrid / WFH Options
Asset Resourcing
with programming languages such as Python or Java. Understanding of data warehousing concepts and data modeling techniques. Experience working with big data technologies (e.g., Hadoop, Spark) is an advantage. Excellent problem-solving and analytical skills. Strong communication and collaboration skills. Responsibilities: Design, build and maintain efficient and scalable data More ❯
with programming languages such as Python or Java. Understanding of data warehousing concepts and data modeling techniques. Experience working with big data technologies (e.g., Hadoop, Spark) is an advantage. Excellent problem-solving and analytical skills. Strong communication and collaboration skills. Benefits Enhanced leave - 38 days inclusive of 8 UK More ❯
data modeling, and ETL/ELT processes. Proficiency in programming languages such as Python, Java, or Scala. Experience with big data technologies such as Hadoop, Spark, and Kafka. Familiarity with cloud platforms like AWS, Azure, or Google Cloud. Excellent problem-solving skills and the ability to think strategically. Strong More ❯
experience in their technologies You have experience in database technologies including writing complex queries against their (relational and non-relational) data stores (e.g. Postgres, Hadoop, Elasticsearch, Graph databases), and designing the database schemas to support those queries You have a good understanding of coding best practices and design patterns More ❯
science, mathematics, finance or equivalent quantitative field - Experience with scripting languages (e.g., Python, Java, R) and big data technologies/languages (e.g. Spark, Hive, Hadoop, PyTorch, PySpark) to build and maintain data pipelines and ETL processes - Demonstrate proficiency in SQL, data analysis, and data visualization tools like Amazon QuickSight More ❯
City of London, London, United Kingdom Hybrid / WFH Options
McCabe & Barton
with have expertise in some of the following: Python, SQL, Scala, and Java for data engineering. Strong experience with big data tools (Apache Spark, Hadoop, Databricks, Dask) and cloud platforms (AWS, Azure, GCP). Proficient in data modelling (relational, NoSQL, dimensional) and DevOps automation (Docker, Kubernetes, Terraform, CI/ More ❯
on mission critical data pipelines and ETL systems. 5+ years of hands-on experience with big data technology, systems and tools such as AWS, Hadoop, Hive, and Snowflake Expertise with common Software Engineering languages such as Python, Scala, Java, SQL and a proven ability to learn new programming languages More ❯
PowerShell, Ruby PREFERRED QUALIFICATIONS - 3+ years of analyzing and interpreting data with Redshift, Oracle, NoSQL etc. experience - Experience with big data technologies such as: Hadoop, Hive, Spark, EMR - Experience with AWS technologies like Redshift, S3, AWS Glue, EMR, Kinesis, FireHose, Lambda, and IAM roles and permissions Our inclusive culture More ❯
infrastructure and its impact on data architecture. Data Technology Skills: A solid understanding of big data technologies such as Apache Spark, and knowledge of Hadoop ecosystems. Knowledge of programming languages such as Python, R, or Java is beneficial. Exposure to ETL/ELT processes, SQL, NoSQL databases is a More ❯
experience in data engineering, including working with AWS services. Proficiency in AWS services like S3, Glue, Redshift, Lambda, and EMR. Knowledge of Cloudera-based Hadoop is a plus. Strong ETL development skills and experience with data integration tools. Knowledge of data modeling, data warehousing, and data transformation techniques. Familiarity More ❯
of database theory and design. Experience with SQL and NoSQL databases. Familiarity with big data technologies and ecosystems such as Microsoft Synapse/Fabric, Hadoop, Spark, Kafka, and others. Skills in machine learning and AI technologies and methodologies. Knowledge of data pipeline tools such as Data Factory, Airflow, and More ❯
in at least one of the following additional languages: Java, C#, C++, Scala Familiarity with Big Data technology in cloud and on-premises environments: Hadoop, HDFS, Spark, NoSQL Databases, Hive, MongoDB, Airflow, Kafka, AWS, Azure, Dockers or Snowflake Good understanding of object-oriented programming (OOP) principles & concepts Familiarity with More ❯
a Data Engineer for Cloud Data Lake activities, especially in high-volume data processing frameworks, ETL development using distributed computing frameworks like Apache Spark, Hadoop, Hive. Experience optimizing database performance, scalability, data security, and compliance. Experience with event-based, micro-batch, and batched high-volume, high-velocity transaction and More ❯
