Data Engineer

We are seeking a highly skilled and motivated Data Engineer with at least

4 years of experience, strong expertise in SQL, Python, and AWS services.

The ideal candidate will have hands-on experience in building,

managing, and optimizing data pipelines and architectures, as well as a deep understanding of

data lake, data warehouse, and lakehouse concepts. You will play a crucial role in designing

and implementing scalable ETL solutions for our data infrastructure, supporting a range of datadriven initiatives.

Key Role and Responsibilities:

• Design and develop scalable and efficient ETL pipelines to process structured and

unstructured data.

• Work extensively with AWS services including S3, Lambda, Step Functions, Glue, EC2, EMR,

SNS, SQS, Redshift, and Athena to manage and optimize data pipelines and workflows.

• Develop, monitor, and troubleshoot workflows using Airflow and other

scheduling/orchestration tools.

• Build and maintain data lakes, data warehouses, and lakehouse architectures, ensuring

seamless integration and optimal data flow.

• Implement and optimize SQL queries and Python scripts for data extraction, transformation,

and loading (ETL).

• Collaborate with data scientists, analysts, and stakeholders to understand business

requirements and translate them into robust data solutions.

• Optimize data pipelines for performance, reliability, and scalability to handle growing

datasets and evolving business needs.

• Leverage best practices for data security, compliance, and governance in cloud-based

environments.

• Perform data quality checks and ensure the accuracy and reliability of the data.

• Stay up to date with the latest technologies and trends in data engineering, cloud platforms,

and ETL development.

Key Qualifications:

• 4+ years of experience as a Data Engineer, ETL Developer, or in a similar role.

• Proficiency in SQL and Python for data manipulation, automation, and processing.

• Extensive experience with AWS services, including:

• S3, Lambda, Step Functions, Glue, EC2, EMR, SNS, SQS, Redshift, and Athena.

• Strong understanding of data lake, data warehouse, and lakehouse concepts and

the ability to design solutions for these architectures.

• Experience with ETL frameworks and tools to build data pipelines.

• Hands-on experience with workflow orchestration tools like Airflow.

• Solid understanding of data governance, security, and compliance best practices.

• Experience in building data models, optimizing databases, and query performance

tuning.

• Familiarity with data lake architectures and modern data integration patterns