Responsibilities

Design, develop, and maintain scalable ETL/ELT pipelines for data ingestion, transformation, validation, and delivery.

Build, schedule, and orchestrate production-grade data workflows using Apache Airflow .

Develop distributed data-processing jobs using Apache Spark .

Write efficient, reusable, and maintainable Python code for data processing, automation, and pipeline development.

Develop and optimize complex SQL queries for data transformation, analysis, and quality validation.

Design pipelines capable of processing large-scale structured, semi-structured, and unstructured datasets.

Implement Redis for caching, high-performance data access, and data-intensive application requirements.

Integrate data from APIs, relational databases, files, third-party providers, and other internal and external sources.

Implement data validation, monitoring, logging, error handling, alerting, and pipeline observability.

Optimize pipeline performance, data storage, processing time, and infrastructure costs.

Develop reusable data-ingestion and transformation frameworks instead of one-off scripts.

Troubleshoot pipeline failures, performance bottlenecks, and data-quality issues to ensure timely resolution.

Collaborate with BI, Product, Data Science, and Engineering teams to deliver reliable, production-ready datasets.

Establish and maintain data-engineering standards, technical documentation, and development best practices.

Preferred Qualification

Experience with cloud data platforms and object storage services such as AWS, Azure, or GCP .

Experience working with PostgreSQL , data warehouses, or analytical databases.

Experience processing geospatial data or large-scale location-based datasets.

Familiarity with DuckDB, Apache Sedona, Trino, Presto , or similar analytical technologies.

Experience processing high-volume event, mobility, transactional, or geospatial data.

Familiarity with CI/CD pipelines and infrastructure-as-code practices.

Experience supporting data products, analytics platforms, or AI/ML pipelines .

Desired Candidate Profile

Bachelor s degree in Computer Science, Software Engineering, Data Science , or a related field.

4 6 years of professional experience in Data Engineering or a closely related role.

Strong hands-on experience designing and implementing production-grade ETL/ELT pipelines .

Strong proficiency in Python for data processing, automation, and data-engineering workflows.

Advanced SQL skills and a strong understanding of relational databases.

Practical production experience with Apache Airflow for workflow orchestration.

Hands-on experience with Apache Spark and distributed data processing.

Strong understanding of data modelling, transformation patterns, and data-pipeline architecture.

Experience with Redis , caching strategies, and high-performance data-access patterns.

Experience processing large datasets and optimizing pipeline and query performance.

Strong understanding of data quality, validation, monitoring, observability, and pipeline reliability.

Familiarity with Linux, Git, Docker/containers , and modern software-engineering practices.

Strong analytical, troubleshooting, communication, and cross-functional collaboration skills.