AWS Cloud Engineer / Big Data Engineer

Dear Candidates,

Greetings to you!!

We have an open position for AWS Cloud Data Engineer position. below is the job description for your reference, kindly let us know your interest in applying for said position with your resume.

About the Role:

As a Big Data Engineer, you will be responsible for building and managing data pipelines, models, and infrastructure to support the growing data needs of the organization. You will work on designing scalable solutions for processing, analyzing, and managing large datasets, including real-time IoT and time-series data.

In this role, you will collaborate with cross-functional teams, including data scientists, analysts, and business stakeholders, to ensure efficient and secure access to data. You will utilize cutting-edge AWS cloud services, data orchestration tools, and big data technologies to build robust, scalable, and high-performing data solutions.

This role is ideal for professionals who are passionate about working with distributed systems, cloud platforms, and advanced data engineering practices.

For 5+ Years (Senior Candidate)

Your Primary Responsibilities Include

• Lead the design, optimization, and implementation of complex data models and ETL processes tailored to client business needs.
• Architect, deploy, and manage highly scalable and secure data infrastructure using AWS services like S3, Redshift, Glue, Kinesis, DynamoDB, and Timestream.
• Design and implement real-time data processing pipelines for IoT and time-series datasets using tools like Kinesis and AWS IoT Core.
• Build, maintain, and optimize workflows using orchestration tools such as Apache Airflow, AWS Step Functions.
• Mentor junior engineers and collaborate with cross-functional teams to ensure seamless data accessibility and utilization.

Required Technical and Professional Expertise

• Extensive experience developing PySpark code for AWS Glue jobs and EMR, with a strong focus on distributed data systems using the Hadoop ecosystem.
• Proficiency in Python and PySpark for building frameworks, custom rule engines, and large-scale data processing workflows.
• Demonstrated expertise in optimizing Spark SQL and Hive queries to minimize batch processing time.
• Strong experience in integrating APIs with Python and handling large datasets using Apache Spark, DataFrames, and RDDs.
• Expertise in AWS services like S3, Lambda, Glue, Kinesis (Data Streams/Firehose), Redshift, DynamoDB, and Timestream for data storage, transformation, and analytics.

Preferred Technical And Professional Expertise

• Proven understanding of DevOps practices, including CI/CD pipelines for data solutions.
• Expertise in designing end-to-end data pipelines for real-time and batch data processing.
• Hands-on experience with time-series databases and IoT data handling, using AWS IoT Core or Timestream.
• Advanced knowledge of object-oriented and functional programming languages like Python, Scala, and Java.
• Experience in Snowflake is added advantage