We are looking for an experienced Lead Data Engineer to architect, build, and optimize large-scale data systems that power analytics and machine learning initiatives across the organization. This role requires a strong technical leader with deep expertise in data engineering, modern data architectures, and cloud-based ecosystems.

Key Responsibilities :

Data Architecture & Pipelines :

- Design and implement scalable data pipelines, data lakes, and data warehouse solutions for large and complex datasets.

ETL/ELT Frameworks :

- Architect efficient ETL/ELT workflows, ensuring best practices for data ingestion, transformation, storage, and governance.

Workflow Optimization :

- Build and manage both batch and real-time data processing pipelines to enable seamless analytics and operational insights.

Collaboration & Accessibility :

- Partner with data scientists, analysts, and backend engineers to ensure data is discoverable, reliable, and ready for use.

Data Modeling & Performance :

- Lead data modeling, schema design, and warehouse optimization to enhance performance and scalability.

Data Quality & Compliance :

- Implement data quality frameworks, observability tools, and governance practices to maintain accuracy, consistency, and compliance.

Leadership & Mentorship :

- Provide technical guidance and mentorship to the data engineering team, conduct code reviews, and drive engineering best practices.

Strategic Collaboration :

- Work closely with product and business leadership to translate data requirements into robust, scalable architecture and actionable insights.

Requirements :

- Experience : 6 - 8 years of hands-on experience in data engineering, including at least 2 years in a lead or mentoring capacity.

- Programming Skills : Strong proficiency in Python or Scala for data pipeline development.

- Database Expertise : Deep knowledge of SQL and experience with relational databases such as PostgreSQL, MySQL, etc.

- Big Data Tools : Proven experience with Apache Spark, Kafka, Airflow, Snowflake, or Redshift.

- Cloud Platforms : Solid understanding of AWS, GCP, or Azure data ecosystems.

- Data Architecture : Expertise in data modeling, schema design, and performance tuning for large-scale systems.

- Governance & Monitoring : Experience implementing data governance, quality checks, and monitoring frameworks.

- DevOps Integration : Familiarity with Docker, Kubernetes, CI/CD pipelines, and Git for modern deployment practices.

- Soft Skills : Excellent analytical thinking, communication, and leadership skills with the ability to drive cross-functional technical initiatives.