Position Summary

Role Overview:

The Azure Data Engineer is an integral part of the technical application development team and primarily responsible for analyze, plan, design, develop, and implement the Azure Data engineering solutions to meet strategic, usability, performance, reliability, control, and security requirements of Data science processes. Requires demonstrable knowledge in areas of Data engineering, AI/ML, Data warehouse and reporting applications. Must be innovative.

Work you will do

1. Perform effort estimation for various implementation and enhancement activities.
2. Perform troubleshooting and problem resolution of applications built and being supported.
3. Excellent written, verbal, listening, analytical, and communication skills are required.
4. Highly self-motivated and directed, Experience in working in team-oriented, collaborative environment.
5. Should take ownership of individual deliverables.
6. Support and coordinate the efforts of Subject Matter Experts, Development, Quality Assurance, Architects and other internal resources for the successful implementation of the projects.
7. Create and maintain internal documentation and end-user training materials as needed.
8. Provide input to standards and guidelines and implement best practices to enable consistency across all projects
9. Participate in the continuous improvement processes as assigned.
10. Ability to recommend innovative solutions to technical problems in a straightforward manner.

Key Responsibilities:

Outcome-Driven Accountability: Understands and champions accountability for both customer and business outcomes, not just technical output. Has led or contributed to data engineering initiatives that delivered impactful, high-quality solutions to complex problems, emphasizing lean design and implementation. Can share examples where these principles resulted in measurable value for customers and the business.

Technical Leadership and Advocacy: Acts as a technical advocate for data products and engineering teams, ensuring alignment with product goals, architecture, and non-functional requirements (NFRs). Participates in and often leads requirement analysis, solution design, development, testing, and integration activities. Actively supports evolutionary releases (such as alpha, beta, MVP) and maintains code integrity and quality throughout the product lifecycle. Can provide examples of technical leadership and advocacy in practice.

Engineering Craftsmanship: Demonstrates a strong commitment to engineering excellence as an individual contributor. Maintains high standards for code design, configuration, and ongoing maintenance. Regularly engages in hands-on work, including code reviews, optimizations, and addressing technical debt within sprints. Continuously learns new technologies and frameworks, experimenting and sharing knowledge with the team. Can provide examples of fostering a culture of craftsmanship and continuous improvement.

Customer-Centric Engineering: Develops lean, effective solutions by engaging directly with customers and product teams, using rapid experimentation to address real needs. Focuses on delivering the right solution at the right time, balancing speed and quality. Can illustrate how customer engagement and iterative feedback have shaped engineering outcomes.

Incremental and Iterative Delivery: Embraces an action-oriented, evidence-driven mindset, favoring incremental progress and learning over exhaustive upfront planning. Navigates complexity and uncertainty by delivering maintainable, supportable solutions in small, validated steps. Can share examples of how this approach has led to successful product delivery.

High-Impact Cross-Functional Collaboration and Integration: Works collaboratively with cross-functional teams—including product management, experience, delivery, infrastructure, and security—to integrate diverse perspectives and make balanced decisions. Fosters a collaborative environment that enhances team synergy, innovation, and shared ownership of outcomes. Can provide examples of effective cross-functional collaboration.

Advanced Technical Proficiency: Possesses deep expertise in modern engineering practices, including Agile, DevSecOps, CI/CD, and advanced deployment strategies (e.g., Blue-Green, Canary). Applies these techniques to optimize solution delivery and minimize waste. Demonstrates proficiency across the full product development lifecycle, with a focus on continuous improvement and learning. Can provide examples of leveraging advanced technical skills to drive product success.

Domain Knowledge: Quickly acquires and applies domain-specific knowledge relevant to the business or product. Translates business and user needs into actionable engineering plans, such as sprint tasks and priorities. Navigates enterprise functions to ensure alignment with organizational goals and product value. Can share examples of translating requirements into impactful engineering solutions.

Effective Communication and Influence: Communicates complex technical concepts clearly and persuasively to both technical and non-technical stakeholders. Supports decision-making through well-structured arguments, evidence, and research. Crafts narratives that align technical solutions with business objectives. Can provide examples of influencing outcomes through effective communication.

Engagement and Collaborative Co-Creation: Engages constructively with stakeholders at all levels, building relationships that foster co-creation and shared momentum toward product goals. Aligns diverse perspectives and drives consensus to achieve feasible, high-value solutions. Can illustrate how collaborative engagement has contributed to successful outcomes.

Education:

1. Bachelor’s degree in Computer Science or Business Information Systems or MCA or equivalent degree.

Qualifications:

1. 2 to 5 years Advanced Level of experience in Azure Data engineering
2. Expertise in Development, deployment and monitoring ADF pipelines (using visual studio and browsers)
3. Expertise in Azure databricks internal programming using (PySpark, SparkR and SparkSQL).
4. Expertise in Debugging Azure Databricks notebooks (including AI/ML).
5. Expertise in managing azure storage (Azure Datalake Gen2, Azure Blob Storage, Azure SQL database)
6. Expertise in Azure logic apps
7. Advanced programming skills in Python, R and SQL (SQL for HANA, MS SQL)
8. Hands on experience in Visualization tools (Tableau / PowerBI)
9. Hands on experience in Data science studios like (Dataiku, Azure ML studio)