Senior Platform Engineer focused on data, cloud platform, and networking to build reliable, secure, and scalable data infrastructure on Azure.
Compensation
This position pays between $101,600 – $152,400 based on experience
Responsibilities
Design, build, test, deploy, monitor, and continuously improve scalable data pipelines using Azure Data Factory, Databricks, Apache Spark, and Python.
Implement Delta Lake patterns on Azure Data Lake Storage including table design, schema evolution, Z-ordering, VACUUM, and partition strategies.
Build and maintain event-driven integrations using Azure Service Bus, Event Hub, and Azure Functions.
Provision and manage Azure infrastructure using infrastructure as code (Terraform, Bicep, or ARM templates).
Design and operate Azure-native services including Azure Data Factory, Azure Databricks, Azure Data Lake Storage Gen2, Microsoft Fabric, Azure Key Vault, and Azure Monitor.
Requirements
Bachelor's degree in Computer Science, Engineering, Information Systems, or equivalent practical experience
Design, develop, test, deploy, and maintain scalable data pipelines using Databricks and related technologies.
Responsibilities
Design, develop, test, deploy, optimize, and support complex, scalable, production-grade data pipelines using Databricks and related data engineering technologies.
Develop robust ETL/ELT solutions to ingest, transform, integrate, and deliver data across cloud and enterprise environments.
Design reusable and maintainable data integration patterns leveraging Databricks, Apache Spark/PySpark, Python, SQL, Delta Lake, APIs, and other appropriate technologies.
Design and maintain efficient data models, data structures, and storage solutions.
Implement data quality, validation, reconciliation, monitoring, and error-handling processes to ensure reliable and accurate data delivery.
Requirements
Bachelor's degree in computer science, Information Systems, Engineering, Data Engineering, or a related field, or equivalent professional experience
We are looking for an experienced Data Engineer to join a project focused on building and developing modern cloud-based data solutions using Microsoft Azure, Azure Data Factory, and Snowflake.
The Power BI Reporting Engineer is responsible for designing, developing, maintaining, and optimizing enterprise business intelligence and reporting solutions using Microsoft Power BI. This role transforms business requirements into interactive dashboards, reports, and analytical solutions.
Compensation
Salary : $105,000~$125,000/y, DOE
Responsibilities
Design, develop, and maintain Power BI reports and dashboards.
Create interactive visualizations, scorecards, and executive dashboards.
Develop reusable report templates and visualization standards.
Optimize report performance and user experience.
Support enterprise reporting initiatives across departments.
Requirements
Bachelor's degree in Computer Science, Information Systems, Business Analytics, or related field
Nice to have: 5+ years of Business Intelligence or Reporting experience
Nice to have: 3+ years of Power BI development experience
Nice to have: Experience building executive and operational dashboards
Nice to have: Experience working with Azure-based data platforms
Design, implement, maintain, and secure Azure-based data platforms, building and managing data pipelines, storage solutions, processing frameworks, and cloud infrastructure to support enterprise reporting, analytics, and business intelligence initiatives.
Compensation
Salary : $105,000~$135,000/y, DOE
Responsibilities
Design, develop, and maintain ETL/ELT pipelines
Build automated batch and near real-time data integration processes
Integrate data from on-premises and cloud systems
Troubleshoot and optimize data workflows
Design and maintain data warehouse and data lake architectures
Requirements
Bachelor's degree in Computer Science, Information Systems, or related field
Nice to have: Power BI environments
Nice to have: Experience migrating on-premises systems to Azure
Design and build scalable ETL/ELT and AI pipelines using Databricks, PySpark, Delta Lake and SQL; develop and maintain Lakehouse architectures; build and optimise Delta Live Tables, Databricks Workflows and job orchestration processes; develop and deploy production-grade Generative AI and agent-based solutions; create and maintain RAG pipelines and AI agent frameworks; support MLOps activities; implement data quality monitoring, governance controls and metadata management through Unity Catalog; develop event-driven integrations using Azure cloud services; collaborate with cross-functional teams to create feature stores and AI training datasets; integrate Databricks with wider Azure services; contribute to data engineering best practices and centre of excellence initiatives; mentor and support engineering teams in AI technologies and capabilities.
Responsibilities
Design and build scalable ETL/ELT and AI pipelines using Databricks, PySpark, Delta Lake and SQL.
Develop and maintain Lakehouse architectures using Bronze, Silver and Gold data layers.
Build and optimise Delta Live Tables, Databricks Workflows and job orchestration processes.
Develop and deploy production-grade Generative AI and agent-based solutions.
Create and maintain RAG pipelines and AI agent frameworks.
Technical owner of the Microsoft Fabric-based Supply Chain Data Platform, responsible for building and developing data integration from SAP and external sources.
Compensation
Attractive salary and a competitive benefits package to reward your expertise and contributions
Responsibilities
Own day-to-day platform operations, monitoring, troubleshooting, and incident resolution across Microsoft Fabric, Azure Data Lake Storage, and source system integrations.
Develop, maintain, and expand our Microsoft Fabric data platform, ensuring reliability, scalability, and performance.
Design and maintain scalable data models, semantic models, and lakehouse structures that support reporting, analytics, and self-service consumption.
Implement robust data ingestion pipelines, primarily using PySpark, from SAP and various external sources.
Collaborate closely with business stakeholders to understand and translate use cases into clear, actionable technical requirements.
EY GDS is seeking a Senior Data Engineer with strong expertise in Databricks, Azure, Python, and SQL to deliver enterprise-scale data modernization and cloud transformation programs, building modern Lakehouse solutions.
Responsibilities
Design, build, and optimize data pipelines using Databricks, PySpark, Python, and SQL.
Develop scalable ETL/ELT solutions and Lakehouse architectures.
Implement solutions using Delta Lake, Unity Catalog, Databricks Workflows, and Structured Streaming.
Develop and support Azure-based data platforms leveraging ADF and ADLS Gen2.
Optimize Spark workloads for performance, scalability, and cost efficiency.
Requirements
Nice to have: Databricks Certified Data Engineer Associate or Professional
Nice to have: Microsoft Azure Data Engineer Associate (DP-203)
Lead the design and development of enterprise-grade data engineering solutions using Microsoft Fabric and Azure.
Responsibilities
Lead the design and development of enterprise-grade data engineering solutions using Microsoft Fabric and Azure.
Design scalable and reliable data pipelines for batch and real-time data processing.
Develop and optimize ETL/ELT processes using modern Azure and Microsoft Fabric technologies.
Work extensively with Microsoft Fabric Data Factory, Lakehouse, Data Warehouse, OneLake, and related Fabric components.
Design and implement solutions using Azure Data Factory, Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure SQL, and other Azure data services.
Requirements
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
Nice to have: Microsoft Certified: Azure Data Engineer Associate
Generous referral bonuses, offering up to PLN6,000 for referring specialists. Additional revenue sharing opportunities for initiating partnerships with new clients.
Responsibilities
Design, build and maintain batch and incremental ETL/ELT pipelines from source systems to Snowflake.
Develop and orchestrate data pipelines using Azure Data Factory, including linked services, datasets, data flows, triggers and reusable parameterised pipelines.
Design and maintain data models in Snowflake, covering staging, core and data mart layers.
Manage Snowflake warehouses, schemas, roles and data-loading mechanisms such as COPY INTO, Snowpipe and stages.
Write, optimise and maintain complex SQL queries for data transformations, reporting and data quality validation.
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