13 of 13 PySpark Jobs in Glasgow

months with possible extensions (No Sponsorship Available ) Skills/Qualifications: · 4+ years of experience developing data pipelines and data warehousing solutions using Python and libraries such as Pandas, NumPy, PySpark, etc. · 3+ years hands-on experience with cloud services, especially Databricks, for building and managing scalable data pipelines · 3+ years of proficiency in working with Snowflake or similar cloud More ❯

data governance, security, and access control within Databricks. • Provide technical mentorship and guidance to junior engineers. Must-Have Skills: • Strong hands-on experience with Databricks and Apache Spark (preferably PySpark). • Proven track record of building and optimizing data pipelines in cloud environments. • Experience with AWS services such as S3, Glue, Lambda, Step Functions, Athena, IAM, and VPC. • Proficiency More ❯

data governance, security, and access control within Databricks. Provide technical mentorship and guidance to junior engineers. Must-Have Skills: Strong hands-on experience with Databricks and Apache Spark (preferably PySpark). Proven track record of building and optimizing data pipelines in cloud environments. Experience with AWS services such as S3, Glue, Lambda, Step Functions, Athena, IAM, and VPC. Proficiency More ❯

data governance, security, and access control within Databricks. Provide technical mentorship and guidance to junior engineers. Must-Have Skills: Strong hands-on experience with Databricks and Apache Spark (preferably PySpark). Proven track record of building and optimizing data pipelines in cloud environments. Experience with AWS services such as S3, Glue, Lambda, Step Functions, Athena, IAM, and VPC. Proficiency More ❯

data engineering, Python development, and cloud-native architecture. YOUR PROFILE Design, develop, and maintain robust data pipelines and ETL workflows using AWS services. Implement scalable data processing solutions using PySpark and AWS Glue. Build and manage infrastructure as code using CloudFormation. Develop and deploy serverless applications using AWS Lambda, Step Functions, and S3. Perform data querying and analysis using More ❯

based modelling workflows and PR reviews via Tabular Editor. Excellent design intuition-clean layouts, drill paths, and KPI logic. Nice to Have Python for automation or ad-hoc prep; PySpark familiarity. Understanding of Lakehouse patterns, Delta Lake, metadata-driven pipelines. Unity Catalog/Purview experience for lineage and governance. RLS/OLS implementation experience. More ❯

based modelling workflows and PR reviews via Tabular Editor. Excellent design intuition-clean layouts, drill paths, and KPI logic. Nice to Have Python for automation or ad-hoc prep; PySpark familiarity. Understanding of Lakehouse patterns, Delta Lake, metadata-driven pipelines. Unity Catalog/Purview experience for lineage and governance. RLS/OLS implementation experience. More ❯

company for the duration of the contract. You must have several years of experience developing data pipelines and data warehousing solutions using Python and libraries such as Pandas, NumPy, PySpark, etc. You will also have a number of years hands-on experience with cloud services, especially Databricks, for building and managing scalable data pipelines. ETL process expertise is essential. More ❯

profiling, ingestion, collation and storage of data for critical client projects. How to develop and enhance your knowledge of agile ways of working and working in open source stack (PySpark/PySql). Quality engineering professionals utilise Accenture delivery assets to plan and implement quality initiatives to ensure solution quality throughout delivery. As a Data Engineering Manager, you will More ❯

delivery of production-grade data engineering solutions. Build, optimise, and maintain large-scale data pipelines. Develop and enhance data lakes and data warehouses. Implement scalable SQL and Python/PySpark-based solutions. Use orchestration tools such as Airflow and AWS Step Functions. Analyse current processes and propose end-to-end technical improvements. Engage with stakeholders to translate business needs … background with hands-on AWS experience across: S3, Lambda, Glue, Step Functions, Athena, SageMaker, VPC, ECS, IAM, KMS AWS CloudFormation (mandatory) UI development experience (mandatory) Strong SQL, Python, and PySpark Experience with GitLab and unit testing Knowledge of modern data engineering patterns and best practices Desirable (Databricks Track) Apache Spark Databricks (Delta Lake, Unity Catalog, MLflow) Experience with Databricks More ❯

a best-in-class Lakehouse from scratch-this is the one. What You'll Be Doing Lakehouse Engineering (Azure + Databricks) Engineer scalable ELT pipelines using Lakeflow Declarative Pipelines , PySpark , and Spark SQL across a full Medallion Architecture (Bronze - Silver - Gold) . Implement ingestion patterns for files, APIs, SaaS platforms (e.g. subscription billing), SQL sources, SharePoint and SFTP using … growing data function. Tech Stack You'll Work With Databricks : Lakeflow Declarative Pipelines, Workflows, Unity Catalog, SQL Warehouses Azure : ADLS Gen2, Data Factory, Key Vault, vNets & Private Endpoints Languages : PySpark, Spark SQL, Python, Git DevOps : Azure DevOps Repos, Pipelines, CI/CD Analytics : Power BI, Fabric What We're Looking For Experience Significant commercial experience of Data Engineering with … years delivering production workloads on Azure + Databricks . Strong PySpark/Spark SQL and distributed data processing expertise. Proven Medallion/Lakehouse delivery experience using Delta Lake . Solid dimensional modelling (Kimball) including surrogate keys, SCD types 1/2, and merge strategies. Operational experience-SLAs, observability, idempotent pipelines, reprocessing, backfills. Mindset Strong grounding in secure Azure Landing More ❯

a best-in-class Lakehouse from scratch-this is the one. ? What You'll Be Doing Lakehouse Engineering (Azure + Databricks) Engineer scalable ELT pipelines using Lakeflow Declarative Pipelines , PySpark , and Spark SQL across a full Medallion Architecture (Bronze ? Silver ? Gold) . Implement ingestion patterns for files, APIs, SaaS platforms (e.g. subscription billing), SQL sources, SharePoint and SFTP using … growing data function. ? Tech Stack You'll Work With Databricks : Lakeflow Declarative Pipelines, Workflows, Unity Catalog, SQL Warehouses Azure : ADLS Gen2, Data Factory, Key Vault, vNets & Private Endpoints Languages : PySpark, Spark SQL, Python, Git DevOps : Azure DevOps Repos, Pipelines, CI/CD Analytics : Power BI, Fabric ? What We're Looking For Experience 5-8+ years of Data Engineering … with 2-3+ years delivering production workloads on Azure + Databricks . Strong PySpark/Spark SQL and distributed data processing expertise. Proven Medallion/Lakehouse delivery experience using Delta Lake . Solid dimensional modelling (Kimball) including surrogate keys, SCD types 1/2, and merge strategies. Operational experience-SLAs, observability, idempotent pipelines, reprocessing, backfills. Mindset Strong grounding More ❯

a best-in-class Lakehouse from scratch-this is the one. What You'll Be Doing Lakehouse Engineering (Azure + Databricks) Engineer scalable ELT pipelines using Lakeflow Declarative Pipelines , PySpark , and Spark SQL across a full Medallion Architecture (Bronze Silver Gold) . Implement ingestion patterns for files, APIs, SaaS platforms (e.g. subscription billing), SQL sources, SharePoint and SFTP using … growing data function. Tech Stack You'll Work With Databricks : Lakeflow Declarative Pipelines, Workflows, Unity Catalog, SQL Warehouses Azure : ADLS Gen2, Data Factory, Key Vault, vNets & Private Endpoints Languages : PySpark, Spark SQL, Python, Git DevOps : Azure DevOps Repos, Pipelines, CI/CD Analytics : Power BI, Fabric What We're Looking For Experience 5-8+ years of Data Engineering … with 2-3+ years delivering production workloads on Azure + Databricks . Strong PySpark/Spark SQL and distributed data processing expertise. Proven Medallion/Lakehouse delivery experience using Delta Lake . Solid dimensional modelling (Kimball) including surrogate keys, SCD types 1/2, and merge strategies. Operational experience-SLAs, observability, idempotent pipelines, reprocessing, backfills. Mindset Strong grounding More ❯