1 to 25 of 47 Apache Spark Jobs in London

/ELT) to support analytics and downstream use cases Developing scalable data solutions using Python , with a focus on maintainability and performance Working with Apache Spark/PySpark to process and transform large datasets Supporting the ingestion, transformation and validation of complex financial data Improving the performance, reliability … That Will Help You Succeed Essential Commercial experience as a Data Engineer at a mid‐level Strong Python development skills Hands‐on experience with Apache Spark/PySpark Solid experience building ETL/ELT pipelines Background within the financial services industry (investment management experience desirable) Comfortable working with ...

keys, sort keys , and workload management. Implement Redshift Spectrum to query data in S3 using external tables. 3. Data Lake & Iceberg Implement and maintain Apache Iceberg tables on AWS for schema evolution and ACID operations. Build Iceberg-based ingestion and transformation pipelines using Glue, EMR, or Spark. Ensure high … data Billing & revenue assurance Build models and pipelines to support network analytics, customer 360, churn prediction, fraud detection , etc. 7. Performance Optimization & Monitoring Tune Spark/Glue jobs for performance and cost. Monitor Redshift/Athena/S3 efficiency and implement best practices. Perform data quality checks and validation ...

platform, features, and best practices with Strong understanding of data modeling principles and techniques 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. Knowledge of ETL/ELT processes ...

pragmatic, impact-focused way What You Bring Senior level Data Engineering experience Excellent Python and SQL skills Experience in big data environments (e.g., Spark, distributed systems, cloud data platforms) Solid understanding of data modeling, ETL/ELT, and pipeline architecture Willingness to work onsite in Edinburgh or London ...

about other roles, please contact Nick Mandella at Harnham. KEYWORDS Python, SQL, AWS, GCP, Azure, Cloud, Databricks, Docker, Kubernetes, CI/CD, Terraform, Pyspark, Spark, Kafka, machine learning, statistics, Data Science, Data Scientist, Big Data, Artificial Intelligence, private equity, finance. ...

discussions and provide guidance on data engineering best practices. Ability to troubleshoot and optimize data workflows. Required Knowledge and Skills: Experience with Databricks or Spark-based environments, including optimization strategies. Familiarity with data governance tools and frameworks (e.g., Unity Catalog). Experience with JavaScript Full-Stack development environments including ...

Responsibilities will include; Design, implement, and optimize end-to-end solutions using Fabric components: o Data Factory (pipelines, orchestration) o Data Engineering (Lakehouse, notebooks, Apache Spark) o Data Warehouse (SQL endpoints, schemas, MPP performance tuning) o Real-Time Analytics (KQL databases, event ingestion) o Manage and enhance OneLake ...

Responsibilities will include; Design, implement, and optimize end-to-end solutions using Fabric components: o Data Factory (pipelines, orchestration) o Data Engineering (Lakehouse, notebooks, Apache Spark) o Data Warehouse (SQL endpoints, schemas, MPP performance tuning) o Real-Time Analytics (KQL databases, event ingestion) o Manage and enhance OneLake ...

Knowledge of machine learning frameworks (e.g., PySpark, PyTorch) and model deployment tools (e.g., MLflow, TensorFlow Serving). Strong experience with data processing frameworks (e.g., Apache Spark, Flink). Expertise in SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Cassandra). Hands-on experience with cloud platforms (e.g. ...

Knowledge of machine learning frameworks (e.g., PySpark, PyTorch) and model deployment tools (e.g., MLflow, TensorFlow Serving). Strong experience with data processing frameworks (e.g., Apache Spark, Flink). Expertise in SQL and NoSQL databases (e.g., MySQL, PostgreSQL, MongoDB, Cassandra). Hands-on experience with cloud platforms (e.g. ...

sectors. Strong knowledge of data fabric architectures, metadata management, and data governance principles. Experience with SQL, ETL tools, and big data technologies (e.g., Hadoop, Spark, Kafka). Familiarity with cloud platforms (AWS, Azure, or Google Cloud) and data integration frameworks. Strong problem-solving skills and ability to work ...

Strong full-stack experience with Python , Java (Spring Boot) , React , SQL, and relational databases. Hands-on experience with modern data engineering tools (e.g. Airflow, Spark, Kafka, dbt, Snowflake or similar). Experience with cloud platforms , containerisation (Docker/Kubernetes) , and CI/CD pipelines . Solid understanding of data ...

data engineering, data platforms and big data technologies Evaluate and improved existing data processes Essential Experiences: Advanced SQL skills Proficient in Databricks, including Spark, Delta Lake and MLflow Experience migrating SSIS to Databricks In depth understanding of ETL/ELT processes Knowledge of data warehousing concepts and architectures Able ...

commodities markets or broader financial markets. Knowledge of quantitative modeling, risk management, or algorithmic trading. Familiarity with big data technologies like Kafka, Hadoop, Spark, or similar. Why Work With Us? Impactful Work: Directly influence the profitability of the business by building technology that drives trading decisions. Innovative Culture ...

TypeScript, JavaScript Machine Learning: TensorFlow, PyTorch, Scikit-learn GenAI/LLMs: OpenAI API, Hugging Face Transformers, LangChain, Langraph, RAG pipelines Data & Analytics: Pandas, NumPy, Spark, Databricks, Hadoop, Vector Databases, Snowflake Model Deployment: MLflow, FastAPI, Flask, Docker, Kubernetes Cloud & Infrastructure: AWS, Azure, GCP CI/CD pipelines (GitLab, Jenkins) Desirable ...

Azure, utilizing managed services like SageMaker or Vertex AI. Data Foundations: Strong understanding of data modeling, feature stores, and distributed computing (e.g., Spark, Dask). Qualifications & Experience Experience: 5+ years in a software or data engineering role, with at least 3 years specifically focused on the deployment and scaling ...

able to engage technical and non-technical stakeholders Nice to Have: Experience with modern analytics and BI platforms Knowledge of big data technologies (e.g. Spark, Hadoop) Experience with real-time or event-driven architectures Familiarity with data cataloguing and lineage tools Experience working in regulated or data-sensitive environments ...

/Q or similar Experience with Airflow, Dagster or similar tools. Exposure to container technologies: Docker, Podman etc Knowledge of 'big data' technologies (Hadoop, Spark, Parquet) Experience with TAQ data, and exchange connectivity. Compensation Anticipated Salary Range for United States: $100,000 - $150,000 base salary, plus eligible ...

Azure or AWS Strong experience designing and delivering data solutions in Databricks Proficient with SQL and Python Experience using Big Data technologies such as Apache Spark or PySpark Great communication skills, effectively participating with Senior Stakeholders Nice to haves: Azure/AWS Data Engineering certifications Databricks certifications What ...

models, transformers, retrieval models). Hands-on experience with LLMs and GenAI technologies. Strong programming and problem-solving skills with proficiency in Python, SQL, Spark, and Hive. Deep understanding of classical and modern ML techniques, A/B testing methodologies, and experiment design. Solid background in ranking, recommendation ...

DevOps and broader software engineering standards. Cloud experience (any platform). Previous mentoring experience. Nice to have: Snowflake or Databricks Spark, PySpark, Hadoop or similar big data tooling BI exposure (PowerBI, Tableau, etc.) Interview Process The process is fully structured, transparent, and efficient: Video call - high-level overview ...

DevOps and broader software engineering standards. Cloud experience (any platform). Previous mentoring experience. Nice to have: Snowflake or Databricks Spark, PySpark, Hadoop or similar big data tooling BI exposure (PowerBI, Tableau, etc.) Interview Process The process is fully structured, transparent, and efficient: Video call - high-level overview ...

from classical statistics and regression to deep learning and NLP. Data Engineering: Proficiency in SQL and experience working with modern data stacks (e.g., Snowflake, Spark, or cloud-native tools on AWS/GCP/Azure). Scientific Rigor: A strong foundation in the scientific methodforming hypotheses, designing experiments ...

Docker and Kubernetes is a plus. Experience designing end to end ML data pipelines. Experience with database technologies such as PostgreSQL, MySQL, or MongoDB, Spark, Databricks and stream data processing such as Kafka, RedPanda is a plus. Direct experience in building and applying large-scale (100M+ users) machine learning ...

Desired Skills (Bonus Points) Experience with MLOps tooling (MLflow, Kubeflow, Weights & Biases, or similar) Building data pipelines (real-time or batch) using tools like Apache Spark, Kafka, Airflow, or dbt Familiarity with recommender systems, transformers, or graph neural networks Exposure to model optimisation techniques (quantisation, distillation, efficient inference ...