Lead Data Engineer
Summary
Lead Data Engineer owning end-to-end delivery of enterprise-scale data platforms on Microsoft Azure — architecture, pipelines, and lakehouse solutions using SQL, Python, PySpark, Synapse, ADF, Delta Lake, and Microsoft Fabric — while leading teams, enforcing governance, and optimizing cloud costs. Requires 10+ years in data engineering and 5+ years of leadership.
Mandatory
Skill Set: SQL + Python + PySpark + Azure Synapse
Role Overview
We are seeking a highly
experienced Lead Data Engineer to drive the strategy, architecture,
delivery, and optimization of enterprise-scale data platforms on Microsoft
Azure. The ideal candidate will combine deep technical expertise with strong
leadership, stakeholder management, and delivery governance capabilities to
build scalable, secure, and cost-efficient data solutions.
Must-Have Skills
- Strong
expertise in SQL, Python, and PySpark - Extensive
experience with Azure Data Factory (ADF) and Azure Synapse Analytics. - Hands-on
experience with Delta Lake architecture and modern data lakehouse
implementations. - Advanced
knowledge of Data Modeling and Data Warehousing concepts. - Strong
exposure to the Microsoft Fabric ecosystem. - Proven
expertise in designing cloud-native data architectures and
enterprise-scale solutions. - Strong design thinking and problem-solving capabilities.
- Working
knowledge of CI/CD pipelines and DevOps practices. - Experience
in cloud cost optimization, performance tuning, and resource governance.
Key Responsibilities
- Lead
and own the end-to-end delivery of enterprise data platforms,
including architecture, development, deployment, and optimization. - Define
and drive the data platform roadmap, ensuring alignment with
business objectives and strategic priorities. - Architect
and govern scalable data pipelines, data models, data warehouses, and
lakehouse solutions using Azure services. - Provide
technical leadership to Data Engineering teams through design reviews,
code reviews, and implementation of engineering best practices. - Drive
the adoption and implementation of Microsoft Fabric, APIs, Azure
Functions, and Python-based solutions. - Ensure
platform scalability, reliability, security, and operational excellence
while optimizing cloud costs. - Establish
and enforce data governance, data quality, security, and compliance
standards. - Implement
and drive CI/CD, DevOps, and Agile delivery practices across the
data engineering landscape. - Collaborate
with business stakeholders, architects, and technology teams to translate
business requirements into scalable technical solutions. - Mentor,
coach, and develop Data Engineering talent, fostering a culture of
continuous learning and innovation. - Leverage GitHub Copilot and AI-assisted engineering tools to improve productivity
and solution quality. - Drive
innovation by evaluating emerging technologies and identifying
opportunities for platform modernization.
Experience
- 10+
years of overall IT experience, including 10+ years in Data Engineering. - Minimum 5+ years of leadership experience managing large-scale Data
Engineering programs and teams. - Proven
track record of leading cross-functional teams and delivering complex
enterprise data transformation initiatives. - Experience
building, hiring, and scaling high-performing Data Engineering teams. - Experience
working within multi-vendor and globally distributed delivery
environments. - Strong
experience in stakeholder management, delivery governance, and
executive-level communication.
Preferred Skills
- Experience
building and managing enterprise APIs and Azure Function Apps. - Exposure
to framework-based data engineering development approaches. - Experience
in solution architecture, stakeholder engagement, and delivery
governance. - Azure
certifications such as: - Azure
Data Engineer Associate - Azure
Solutions Architect Expert - Familiarity
with MLOps, Data Science, and AI-driven analytics solutions. - Hands-on
experience with Power BI and enterprise reporting platforms. - Experience
leveraging AI and Generative AI capabilities within Data Engineering
ecosystems.
Key Leadership Traits
- Strong
ownership mindset with a focus on accountability and business outcomes. - Excellent
communication, stakeholder management, and influencing skills. - Ability
to balance technical leadership with delivery and people management
responsibilities. - Strategic
thinker with a strong focus on execution excellence. - Strong
conflict resolution and cross-functional collaboration skills. - Adaptability
and resilience in fast-paced, evolving environments. - Passion
for mentoring teams and fostering a culture of innovation and continuous
improvement.
Skills
- Agile
- AI
- Analytics
- API
- Azure
- Azure Data Factory
- Azure Functions
- Azure Synapse
- CI/CD
- Cloud
- Cloud Native
- Data Engineering
- Data Governance
- Data Modeling
- Data Pipelines
- Data Quality
- Data Science
- Data Warehousing
- Delta Lake
- Design Thinking
- DevOps
- Generative AI
- GitHub
- Github Copilot
- Lakehouse
- Microsoft Fabric
- MLOps
- Power BI
- PySpark
- Python
- SQL
- Stakeholder Management