. Hands-on experience with 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 NoSQL and graph databases. Unix 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 ❯
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 ❯
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 ❯
tools like Apache NiFi, Talend, or custom scripts. Familiarity with ELT (Extract, Load, Transform) processes is a plus. Big Data Technologies : Familiarity with big data frameworks such as ApacheHadoop and Apache Spark, including experience with distributed computing and data processing. Cloud Platforms: Proficient in using cloud platforms (e.g., AWS, Google Cloud Platform, Microsoft Azure) for data storage, processing 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) 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 ❯
Microsoft 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 ❯
S3, Lambda, BigQuery, or Databricks. 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 skills. Bachelor's degree in More ❯
in SQL & Database design constructs, entity relationship modeling, dimensional modeling. • Experience in Relational and NoSQL databases (e.g., Oracle, Sybase, SQL Server, PostgreSQL, MongoDB) • Experienced in big data technologies like Hadoop & Snowflake • Prior experience of working with ETL tools or as a SQL developer. • Hands-on experience working with reporting and analytics tools such as Tableau and Python • Familiarity with More ❯
pipelines and ETL - informatica - Experience in SQL and database management systems - Knowledge of data modelling , warehousing concepts , and ETL processes - Experience with big data technologies and frameworks such as Hadoop, Hive, Spark. Programming experience in Python or Scala. - Demonstrated analytical and problem-solving skills. - Familiarity with cloud platforms (e.g Azure , AWS ) and their data related services - Proactive and detail More ❯
London, England, United Kingdom Hybrid / WFH Options
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and statistical packages. Strong analytical, problem-solving, and critical thinking skills. 8.Experience with social media analytics and understanding of user behaviour. 9.Familiarity with big data technologies, such as ApacheHadoop, Apache Spark, or Apache Kafka. 10.Knowledge of AWS machine learning services, such as Amazon SageMaker and Amazon Comprehend. 11.Experience with data governance and security best practices in AWS. 12Excellent More ❯
professional experience Preferred Skills: Experience working within the public sector. Knowledge of cloud platforms (e.g., IBM Cloud, AWS, Azure). Familiarity with big data processing frameworks (e.g., Apache Spark, Hadoop). Understanding of data warehousing concepts and experience with tools like IBM Cognos or Tableau. Certifications:While not required, the following certifications would be highly beneficial: Experience working within … the public sector. Knowledge of cloud platforms (e.g., IBM Cloud, AWS, Azure). Familiarity with big data processing frameworks (e.g., Apache Spark, Hadoop). Understanding of data warehousing concepts and experience with tools like IBM Cognos or Tableau. ABOUT BUSINESS UNIT IBM Consulting is IBM's consulting and global professional services business, with market leading capabilities in business and 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 ❯
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 ❯
environment. Preferred Qualifications: AWS Certified 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 structure. Join a rapidly expanding More ❯
experience within either Flask, Tornado or Django, Docker Experience working with ETL pipelines is desirable e.g. Luigi, Airflow or Argo Experience with big data technologies, such as Apache Spark, Hadoop, Kafka, etc Data acquisition and development of data sets and improving data quality Preparing data for predictive and prescriptive modelling Hands on coding experience, such as Python Reporting tools More ❯
years of experience • Working experience in Palantir Foundry platform is must • Experience designing and implementing data analytics solutions on enterprise data platforms and distributed computing (Spark/Hive/Hadoop preferred). • Proven track record of understanding and transforming customer requirements into a best-fit design and architecture. • Demonstrated experience in end-to-end data management, data modelling, and More ❯
with programming languages such as Python and R, and ML libraries (TensorFlow, PyTorch, scikit-learn). Hands-on experience with cloud platforms (Azure ML) and big data ecosystems (e.g., Hadoop, Spark). Strong understanding of CI/CD pipelines, DevOps practices, and infrastructure automation. Familiarity with database systems (SQL Server, Snowflake) and API integrations. Strong skills in ETL processes 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 ❯
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 ❯
for the last 10 years, and ability to obtain security clearance. Preferred Skills Experience with cloud platforms (IBM Cloud, AWS, Azure). Knowledge of big data frameworks (Apache Spark, Hadoop). Experience with data warehousing tools like IBM Cognos or Tableau. Certifications in relevant technologies are a plus. Additional Details Seniority level: Mid-Senior level Employment type: Full-time More ❯
5+ years of experience working 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 Experience with workflow orchestration tools 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 ❯
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 ❯
