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 More ❯
Scala. ? AI Frameworks: Extensive experience with AI frameworks and libraries, including TensorFlow, PyTorch, or similar. ? Data Processing: Expertise in big data technologies such as ApacheSpark, Hadoop, and experience with data pipeline tools like Apache Airflow. ? Cloud Platforms: Strong experience with cloud services, particularly AWS, Azure, or More ❯
to Have: AWS Certified Data Engineer, or AWS Certified Data Analytics, or AWS Certified Solutions Architect Experience with big data tools and technologies like ApacheSpark, Hadoop, and Kafka Knowledge of CI/CD pipelines and automation tools such as Jenkins or GitLab CI About Adastra For more More ❯
e.g., Refinitiv, Bloomberg). Data Platforms: Warehouses: Snowflake, Google BigQuery, or Amazon Redshift. Analytics: Tableau, Power BI, or Looker for client reporting. Big Data: ApacheSpark or Hadoop for large-scale processing. AI/ML: TensorFlow or Databricks for predictive analytics. Integration Technologies: API Management: Apigee, AWS API More ❯
e.g., Refinitiv, Bloomberg). Data Platforms: Warehouses: Snowflake, Google BigQuery, or Amazon Redshift. Analytics: Tableau, Power BI, or Looker for client reporting. Big Data: ApacheSpark or Hadoop for large-scale processing. AI/ML: TensorFlow or Databricks for predictive analytics. Integration Technologies: API Management: Apigee, AWS API More ❯
and contribute to code reviews and best practices Skills & Experience Strong expertise in Python and SQL for data engineering Hands-on experience with Databricks, Spark, Delta Lake, Delta Live Tables Experience in batch and real-time data processing Proficiency with cloud platforms (AWS, Azure, Databricks) Solid understanding of data More ❯
delivery across a range of projects, including data analysis, extraction, transformation, and loading, data intelligence, data security and proven experience in their technologies (e.g. Spark, cloud-based ETL services, Python, Kafka, SQL, Airflow) You have experience in assessing the relevant data quality issues based on data sources & uses cases More ❯
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 More ❯
Qualifications: Master's or Ph.D. degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related fields. Proven experience in Databricks and its ecosystem (Spark, Delta Lake, MLflow, etc.). Strong proficiency in Python and R for data analysis, machine learning, and data visualization. In-depth knowledge of cloud … BigQuery, Redshift, Data Lakes). Expertise in SQL for querying large datasets and optimizing performance. Experience working with big data technologies such as Hadoop, ApacheSpark, and other distributed computing frameworks. Solid understanding of machine learning algorithms, data preprocessing, model tuning, and evaluation. Experience in working with LLM More ❯
london, south east england, United Kingdom Hybrid / WFH Options
Careerwise
Qualifications: Master's or Ph.D. degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related fields. Proven experience in Databricks and its ecosystem (Spark, Delta Lake, MLflow, etc.). Strong proficiency in Python and R for data analysis, machine learning, and data visualization. In-depth knowledge of cloud … BigQuery, Redshift, Data Lakes). Expertise in SQL for querying large datasets and optimizing performance. Experience working with big data technologies such as Hadoop, ApacheSpark, and other distributed computing frameworks. Solid understanding of machine learning algorithms, data preprocessing, model tuning, and evaluation. Experience in working with LLM More ❯
City of London, London, United Kingdom Hybrid / WFH Options
McCabe & Barton
ideal candidate with have expertise in some of the following: Python, SQL, Scala, and Java for data engineering. Strong experience with big data tools (ApacheSpark, Hadoop, Databricks, Dask) and cloud platforms (AWS, Azure, GCP). Proficient in data modelling (relational, NoSQL, dimensional) and DevOps automation (Docker, Kubernetes More ❯
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. Strong More ❯
for engineering practices, such as meta driven intelligent ETL and pipeline processes. Strong skills in relevant programming languages, frameworks, and platforms including SQL, Python, Spark, R etc. A strong track record of achievement in data engineering. Experience/understanding of data lifecycle management frameworks and project management methodology. Practical More ❯
engineering, including infrastructure-as-code (e.g., Terraform, CloudFormation), CI/CD pipelines, and monitoring (e.g., CloudWatch, Datadog). Familiarity with big data technologies like ApacheSpark, Hadoop, or similar. ETL/ELT tools and creating common data sets across on-prem (IBMDatastage ETL) and cloud data stores Leadership More ❯
practices to improve data engineering processes. Experience Required: Developing data processing pipelines in python and SQL for Databricks including many of the following technologies: Spark, Delta, Delta Live Tables, PyTest, Great Expectations (or similar) and Jobs. Developing data pipelines for batch and stream processing and analytics. Building data pipelines More ❯
practices to improve data engineering processes. Experience Required: Developing data processing pipelines in python and SQL for Databricks including many of the following technologies: Spark, Delta, Delta Live Tables, PyTest, Great Expectations (or similar) and Jobs. Developing data pipelines for batch and stream processing and analytics. Building data pipelines More ❯
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 More ❯
Learning (ML): Deep understanding of machine learning principles, algorithms, and techniques. Experience with popular ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark. Proficiency in data preprocessing, feature engineering, and model evaluation. Knowledge of ML model deployment and serving strategies, including containerization and microservices. Familiarity with More ❯
and optimizing SQL Knowledge of AWS services including S3, Redshift, EMR, Kinesis and RDS Experience with Open Source Data Technologies (Hadoop, Hive, Hbase, Pig, Spark, etc.) Ability to write code in Python, Ruby, Scala or other platform-related Big data technology Knowledge of professional software engineering practices & best practices More ❯
Learning (ML): • Deep understanding of machine learning principles, algorithms, and techniques. • Experience with popular ML frameworks and libraries like TensorFlow, PyTorch, scikit-learn, or Apache Spark. • Proficiency in data preprocessing, feature engineering, and model evaluation. • Knowledge of ML model deployment and serving strategies, including containerization and microservices. • Familiarity with More ❯
other advanced analytics infrastructure. Familiarity with infrastructure-as-code (IaC) tools such as Terraform or CloudFormation. Experience with modern data engineering technologies (e.g., Kafka, Spark, Flink, etc.). Why join YouLend? Award-Winning Workplace: YouLend has been recognised as one of the "Best Places to Work 2024" by the More ❯
ensure high availability and accessibility. Experience & Skills : Strong experience in data engineering. At least some commercial hands-on experience with Azure data services (e.g., ApacheSpark, Azure Data Factory, Synapse Analytics). Proven experience in leading and managing a team of data engineers. Proficiency in programming languages such More ❯
