
Job Overview:
We are seeking an experienced AWS Data Engineer to join our growing data team. The ideal candidate will have hands-on experience with AWS Glue, Redshift, PySpark, and other AWS services to build robust, scalable data pipelines. This role is perfect for someone passionate about data engineering, automation, and cloud-native development.
Key Responsibilities:
- Design, build, and maintain scalable and efficient ETL pipelines using AWS Glue, PySpark, and related tools.
- Integrate data from diverse sources and ensure its quality, consistency, and reliability.
- Work with large datasets in structured and semi-structured formats across cloud-based data lakes and warehouses.
- Optimize and maintain data infrastructure, including Amazon Redshift, for high performance.
- Collaborate with data analysts, data scientists, and product teams to understand data requirements and deliver solutions.
- Automate data validation, transformation, and loading processes to support real-time and batch data processing.
- Monitor and troubleshoot data pipeline issues and ensure smooth operations in production environments.
Required Skills:
- 5 to 7 years of hands-on experience in data engineering roles.
- Strong proficiency in Python and PySpark for data transformation and scripting.
- Deep understanding and practical experience with AWS Glue, AWS Redshift, S3, and other AWS data services.
- Solid understanding of SQL and database optimization techniques.
- Experience working with large-scale data pipelines and high-volume data environments.
- Good knowledge of data modeling, warehousing, and performance tuning.
Preferred/Good to Have:
- Experience with workflow orchestration tools like Airflow or Step Functions.
- Familiarity with CI/CD for data pipelines.
- Knowledge of data governance and security best practices on AWS.

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Job description
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- Support platform modernization and continuous improvement initiatives
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Qualifications
Bachelor’s or master’s degree in computer science, Information Technology, or a related fields
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• Warehouse Inventory collection:
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Ideal Candidate Profile:
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Company Description
Role Description
Qualifications
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Head Office: Koramangala, Bengaluru, Karnataka, India.
Work Location: Work From Home
Working Days: Monday to Friday
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About Us:
We’re looking to hire someone to help scale Machine Learning and NLP efforts at Episource. You’ll work with the team that develops the models powering Episource’s product focused on NLP driven medical coding. Some of the problems include improving our ICD code recommendations , clinical named entity recognition and information extraction from clinical notes.
This is a role for highly technical engineers who combine outstanding oral and written communication skills, and the ability to code up prototypes and productionalize using a large range of tools, frameworks, and languages. Most importantly they need to have the ability to autonomously plan and organize their work assignments based on high-level team goals.
What you will do at Episource:
You will be responsible for setting an agenda to develop and build machine learning platforms that positively impact the business, working with partners across the company including operations and engineering. You will be working closely with the machine learning team to design and implement back end components and services. You will be evaluating new technologies, enhancing the applications, and providing continuous improvements to produce high quality software.
Required Skills:
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Strong background in analytics, BI or data science deployments is preferable with 2-6 years of experience
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Knowledge of React/Vue, HTML, CSS
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Experience building and consuming APIs
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Experience with MySQL, MongoDB and MEAN stack
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Knowledge and experience with serverless architectures is a plus
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Hands-on experience with AWS or any major cloud service provider for deploying solutions
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Experience with Docker or Kubernetes in deploying solutions on the cloud
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Hands-on experience Python, Apache Spark & Big Data platforms to manipulate large-scale structured and unstructured datasets.
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Fluent in data fundamentals: SQL, data manipulation using a procedural language, statistics, experimentation, and modeling
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