Data Engineer Lead

What your main responsibilities are:

Design, build, and optimize scalable data engineering solutions that support analytics, reporting, and data-driven decision-making across the organization. Lead the architecture, implementation, and continuous improvement of modern data platforms and pipelines that integrate structured and unstructured data from multiple sources across both on-premises and cloud environments.

Serve as a hands-on leader with both technical depth and business understanding, guiding a team of data engineers, mentoring junior members, and fostering a culture of collaboration, innovation, and accountability. Work closely with business and analytics partners to understand functional objectives, ensuring that data solutions are practical, aligned with priorities, and enable effective use of data for decision-making.

Lead enterprise-wide efforts to modernize data infrastructure, driving the transition from legacy systems to modern, hybrid architectures using Azure, Databricks, and other emerging technologies. Champion the adoption of new technologies, scalable design patterns, and automation frameworks that enhance performance, data quality, and accessibility.

Act as a bridge between technical and business teams, translating complex data concepts into clear, actionable insights. Communicate findings and recommendations to senior stakeholders in a concise, meaningful way that connects data outcomes to business goals. Drive the adoption of best practices in data engineering, enforce data governance and quality standards, and ensure the scalability, reliability, and efficiency of enterprise data systems across hybrid environments.

What you’ll be working on -

Data Architecture & Engineering

Architect, design, and implement robust, high-performance data pipelines and data platforms using Azure Data Factory, Databricks, Synapse, and on-premise technologies. Ensure scalability, efficiency, and cost optimization across the hybrid data ecosystem.

ETL/ELT Development & Automation

Build and automate data ingestion, transformation, and integration processes from multiple systems (RDBMS, APIs, unstructured data). Leverage best practices in ETL/ELT design to deliver reliable, reusable, and well-documented pipelines.

Cloud Migration & Modernization

Lead strategic initiatives to migrate legacy on-premise data systems to modern, cloud- enabled or hybrid architectures. Evaluate emerging technologies, recommend modernization paths, and drive seamless transitions while minimizing disruption to business operations.

Collaboration & Mentorship

Collaborate closely with analysts, data scientists, and business stakeholders to ensure data solutions meet analytical and functional needs. Provide mentorship and guidance to junior team members, fostering technical growth, ownership, and continuous improvement.

Data Modeling & Governance

Design and maintain scalable data models, enforce governance standards, and ensure high data quality and lineage across environments. Support enterprise metadata management, cataloging, and compliance requirements.

Monitoring, Optimization & Reliability

Implement observability and performance monitoring for data pipelines. Proactively identify and address bottlenecks, ensuring high reliability, scalability, and efficiency in production systems.

Presentation & Communication

Present technical recommendations and analytic insights to senior leadership with clear storytelling and visualization. Translate technical details into business impact and actionable outcomes.

What we are looking for

Education

Bachelor’s degree in computer science, Information Systems, Engineering, or a quantitative discipline such as Mathematics or Statistics. A Master’s degree in a relevant field is preferred.

Experience:

Minimum 8+ years of experience in data engineering, data architecture, or big data environments, including experience leading technical teams or projects. Proven ability to deliver complex data initiatives end-to-end.

Technical Skills:

- Strong proficiency in SQL, Python, and PySpark

-Advanced experience with Azure Data Services (Data Factory, Databricks, Synapse, Data Lake, Event Hubs) and familiarity with on-premise tools and data platforms

- Proven experience in ETL/ELT development, data modeling, data warehousing, and pipeline automation

- Familiarity with DevOps for Data, CI/CD, Infrastructure as Code (e.g., Terraform, ARM templates)

- Experience with real-time data streaming (Kafka, Event Hubs, or similar)-

- Understanding of data governance, data quality frameworks, and security best practices

-Exposure to machine learning pipelines, AI integration, and advanced analytics enablement is a plus

Good to Have:

- Experience with Delta Lake, Unity Catalog, and data mesh concepts

-Working knowledge of Power BI and Tableau integration with data platforms

-Familiarity with Azure ML, Airflow, or Kubernetes-based data workflows