Data Architect for Scalable Data Engineering

1 day ago


Bengaluru, Karnataka, India Freo Full time

About Freo

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Freo is a cutting-edge neobank that is revolutionizing digital banking with an innovative approach tailored to the unique needs of modern consumers in India and SE Asia. With a range of financial products such as a digital savings account, flexible credit lines, credit cards, and EMI cards, buy now pay later (BNPL) schemes, all in one place, Freo empowers its users to take control of their finances and achieve their goals.

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As a leading developer of technology platforms and mobile applications, MWYN Tech has partnered with top banks and regulated financial institutions to provide seamless solutions. While Freo is not a bank itself and does not hold a banking license, it partners with trusted entities to offer secure and reliable services.

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Job Description

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We are seeking an experienced and skilled Senior Data Engineer to join our data engineering team. This role involves designing, developing, and maintaining a robust and scalable data architecture to support both batch and real-time data processing needs.

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Key Responsibilities:

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  • Data Lake and Warehouse Architecture: Design, implement, and maintain data lakes and data warehouses ensuring high availability, performance, and scalability.">">
  • ETL Development: Lead the development of ETL pipelines for batch processing as well as real-time streaming data integration to support the organization's data needs.">">
  • Data Engineering Best Practices: Establish and enforce best practices for data engineering, data security, and data quality management.">">
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Requirements:

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To be successful in this role, you will need 5-7 years of experience in data engineering or related fields with hands-on experience in data lake and data warehouse architecture.

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Tech Skills Required:

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  • Proficiency with AWS services including Redshift, S3 Lambda, etc.">">
  • Experience with other AWS big data tools such as EMR, Kinesis, Firehose, Glue.">">
  • Strong experience with PySpark for data lake management and data transformation.">">
  • Hands-on expertise in developing ETL pipelines for both batch and real-time processing.">">
  • Management experience with Data Engineering tools such as Apache Superset, Apache Airflow, Apache Spark, Jupyterhub, etc.">">
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Estimated Salary Range: $120,000 - $180,000 per year, based on location and industry standards.



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