technical concepts to non-technical stakeholders. Team Player: Ability to work effectively in a collaborative team environment, as well as independently. Preferred Qualifications: Familiarity with big data technologies (e.g., Hadoop, Spark, Kafka). Familiarity with AWS and its data services (e.g. S3, Athena, AWS Glue). Familiarity with data warehousing solutions (e.g., Redshift, BigQuery, Snowflake). Knowledge of containerization More ❯
Azure, or Google Cloud Platform (GCP). Strong proficiency in SQL and experience with relational databases such as MySQL, PostgreSQL, or Oracle. Experience with big data technologies such as Hadoop, Spark, or Hive. Familiarity with data warehousing and ETL tools such as Amazon Redshift, Google BigQuery, or Apache Airflow. Proficiency in Python and at least one other programming language More ❯
Azure technologies. Proficiency in Azure data services (Azure SQL Database, Azure Synapse Analytics, Azure Data Factory, Azure Databricks). Experience with data modeling, data warehousing, and big data processing (Hadoop, Spark, Kafka). Strong understanding of SQL and NoSQL databases, data modeling, and ETL/ELT processes. Proficiency in at least one programming language (Python, C#, Java). Experience More ❯
Microsoft Azure, or Google Cloud Platform (GCP). Proficiency in SQL and experience with relational databases such as MySQL, PostgreSQL, or Oracle. Experience with big data technologies such as Hadoop, Spark, or Hive. Familiarity with data warehousing and ETL tools such as Amazon Redshift, Google BigQuery, or Apache Airflow. Proficiency in at least one programming language such as Python More ❯
data processing tools. Strong technical proficiency in data modeling, SQL, NoSQL databases, and data warehousing. Hands-on experience with data pipeline development, ETL processes, and big data technologies (e.g., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Google Cloud and cloud-based data services (e.g., AWS Redshift, Azure Synapse Analytics, Google BigQuery). Experience More ❯
south west london, south east england, united kingdom
Mars
data processing tools. Strong technical proficiency in data modeling, SQL, NoSQL databases, and data warehousing. Hands-on experience with data pipeline development, ETL processes, and big data technologies (e.g., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Google Cloud and cloud-based data services (e.g., AWS Redshift, Azure Synapse Analytics, Google BigQuery). Experience More ❯
of the following: Python, SQL, 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 frameworks, CI/CD, and More ❯
Understanding of ML development workflow and knowledge of when and how to use dedicated hardware. Significant experience with Apache Spark or any other distributed data programming frameworks (e.g. Flink, Hadoop, Beam) Familiarity with Databricks as a data and AI platform or the Lakehouse Architecture. Experience with data quality and/or and data lineage frameworks like Great Expectations, dbt More ❯
engineering, architecture, or platform management 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 and implementation. Hands-on knowledge More ❯
time data pipelines for processing 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 ML inference pipelines into production More ❯
Strong knowledge of data architecture, 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 communication and interpersonal skills, with More ❯
Experience of Relational Databases and Data Warehousing concepts. Experience of Enterprise ETL tools such as Informatica, Talend, Datastage or Alteryx. Project experience using the any of the following technologies: Hadoop, Spark, Scala, Oracle, Pega, Salesforce. Cross and multi-platform experience. Team building and leading. You must be: Willing to work on client sites, potentially for extended periods. Willing to More ❯
in data modelin g , SQL, NoSQL databases, and data warehousing . Hands-on experience with data pipeline development, ETL processes, and big data technolo g ies (e. g ., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Goo g le Cloud and cloud-based data services (e.g ., AWS Redshift, Azure Synapse Analytics, Goog More ❯
in data modelin g , SQL, NoSQL databases, and data warehousing . Hands-on experience with data pipeline development, ETL processes, and big data technolo g ies (e. g ., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Goo g le Cloud and cloud-based data services (e.g ., AWS Redshift, Azure Synapse Analytics, Goog More ❯
in data modelin g , SQL, NoSQL databases, and data warehousing . Hands-on experience with data pipeline development, ETL processes, and big data technolo g ies (e. g ., Hadoop, Spark, Kafka). Proficiency in cloud platforms such as AWS, Azure, or Goo g le Cloud and cloud-based data services (e.g ., AWS Redshift, Azure Synapse Analytics, Goog More ❯
Science, Data Science, Engineering, or a related field. Strong programming skills in languages such as Python, SQL, or Java. Familiarity with data processing frameworks and tools (e.g., Apache Spark, Hadoop, Kafka) is a plus. Basic understanding of cloud platforms (e.g., AWS, Azure, Google Cloud) and their data services. Knowledge of database systems (e.g., MySQL, PostgreSQL, MongoDB) and data warehousing More ❯
Snowflake. Understanding of cloud platform 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 nice-to-have, providing a More ❯
DataStage, Talend and Informatica. Ingestion mechanism like Flume & Kafka. Data modelling – Dimensional & transactional modelling using RDBMS, NO-SQL and Big Data technologies. Data visualization – Tools like Tableau Big data – Hadoop eco-system, Distributions like Cloudera/Hortonworks, Pig and HIVE Data processing frameworks – Spark & Spark streaming Hands-on experience with multiple databases like PostgreSQL, Snowflake, Oracle, MS SQL Server More ❯
DataStage, Talend and Informatica. Ingestion mechanism like Flume & Kafka. Data modelling – Dimensional & transactional modelling using RDBMS, NO-SQL and Big Data technologies. Data visualization – Tools like Tableau Big data – Hadoop eco-system, Distributions like Cloudera/Hortonworks, Pig and HIVE Data processing frameworks – Spark & Spark streaming Hands-on experience with multiple databases like PostgreSQL, Snowflake, Oracle, MS SQL Server More ❯
7+ years in data architecture and solution design, and a history of large-scale data solution implementation. Technical Expertise : Deep knowledge of data architecture principles, big data technologies (e.g., Hadoop, Spark), and cloud platforms like AWS, Azure, or GCP. Data Management Skills : Advanced proficiency in data modelling, SQL/NoSQL databases, ETL processes, and data integration techniques. Programming & Tools More ❯
Statistics, Maths or similar Science or Engineering discipline Strong Python and other programming skills (Java and/or Scala desirable) Strong SQL background Some exposure to big data technologies (Hadoop, spark, presto, etc.) NICE TO HAVES OR EXCITED TO LEARN: Some experience designing, building and maintaining SQL databases (and/or NoSQL) Some experience with designing efficient physical data More ❯
and-loss forecasting and planning for the Physical Consumer business. We are building the next generation Business Intelligence solutions using big data technologies such as Apache Spark, Hive/Hadoop, and distributed query engines. As a Data Engineer in Amazon, you will be working in a large, extremely complex and dynamic data environment. You should be passionate about working More ❯
Expertise : Strong foundation in data engineering, data analytics, or data science, with the ability to work effectively with various data types and sources. Experience using big data technologies (e.g. Hadoop, Spark, Hive) and database management systems (e.g. SQL and NoSQL). Graph Database Expertise : Deep understanding of graph database concepts, data modeling, and query languages (e.g., Cypher). Demonstrate More ❯
Familiarity with Data Mesh, Data Fabric, and product-led data strategies. Expertise in cloud platforms (AWS, Azure, GCP, Snowflake). Technical Skills Proficiency in big data tools (Apache Spark, Hadoop). Programming knowledge (Python, R, Java) is a plus. Understanding of ETL/ELT, SQL, NoSQL, and data visualisation tools. Awareness of ML/AI integration into data architectures. More ❯
Docker Experience with NLP and/or computer vision Exposure to cloud technologies (eg. AWS and Azure) Exposure to Big data technologies Exposure to Apache products eg. Hive, Spark, Hadoop, NiFi Programming experience in other languages This is not an exhaustive list, and we are keen to hear from you even if you don't tick every box. The More ❯
