At a Glance
- Tasks: Design and develop analytics-ready data products using Microsoft Azure technologies.
- Company: Join a forward-thinking tech company focused on data innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on cutting-edge technology and career advancement.
- Why this job: Make an impact by building scalable data solutions that drive business decisions.
- Qualifications: 9+ years of experience in data solutions and strong skills in Azure and Python.
The predicted salary is between 123750 - 151250 $ per year.
The Data Product Analyst designs, develops, and operates trusted, analytics-ready data products on Microsoft Azure. This role builds scalable data pipelines and curated datasets using Azure Data Factory, Databricks, Synapse Analytics, Delta Lake, Python, and SQL to support reporting, self-service analytics, data science, and business decision-making. The position collaborates with product owners and cross-functional teams to ensure that data products are secure, governed, reliable, and aligned with organizational objectives.
Key Responsibilities
- Collaborate with business stakeholders, product owners, architects, and delivery teams to define data product objectives, requirements, success criteria, and implementation priorities.
- Design and implement secure, scalable data solutions using Azure Storage, Azure SQL, Azure Synapse Analytics, networking services, service principals, and managed identities.
- Develop and optimize enterprise-grade ingestion, transformation, and orchestration pipelines using Azure Data Factory, Azure Databricks, Python, PySpark, SQL, and Delta Lake.
- Build and manage Databricks Workflows, Jobs, Notebooks, clusters, and related processing environments to support reliable and high-volume data operations.
- Design analytics-ready datasets, data marts, semantic layers, and data products for self-service reporting, data science, machine learning, and operational analytics.
- Implement data quality, security, governance, monitoring, error handling, performance optimization, and operational support processes for data products and pipelines.
- Use AI and automation tools to improve code generation, data analysis, testing, documentation, monitoring, and productivity throughout the data engineering lifecycle.
Required Qualifications
- Minimum 9 years of relevant experience.
- Minimum 3 years of experience designing data solutions for trusted, analytics-ready datasets using tools such as Power BI and Synapse, including semantic layers, data marts, and data products.
- Minimum 4 years of experience using Git or GitHub for version control, collaborative development, code management, code review, and integration with data engineering workflows.
- Minimum 3 years of experience using Azure services such as Storage, SQL, Synapse, and networking to implement scalable and secure data solutions.
- Demonstrated experience implementing secure authentication for data pipelines and integrations using service principals and managed identities.
- Minimum 5 years of experience using Python, including PySpark, and SQL to develop, orchestrate, optimize, and troubleshoot enterprise-grade ETL/ELT workflows.
- Minimum 3 years of hands-on experience with Azure Databricks, Delta Lake, Workflows, Jobs, Notebooks, and cluster management.
- Minimum 1 year of experience using AI for code generation, data analysis, automation, productivity improvement, and data engineering workflow support.
Preferred Qualifications
- Minimum 6 years of experience performing business requirements analysis related to data manipulation, transformation, cleansing, and data wrangling.
- Minimum 6 years of experience with Microsoft SQL Server, including database design, optimization, and administration in enterprise environments.
- Experience with Microsoft Fabric and Azure Synapse Analytics, RESTful API integration, message queueing technologies such as ActiveMQ or Azure Service Bus, and cross-functional delivery of software applications and data products.
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