Senior Data Engineer (AWS, Spark)

Description

We are seeking a Senior Data Engineer to design, build, and maintain large-scale data pipelines and distributed processing systems that power AI-driven initiatives. In this role, you will leverage your expertise in AWS, Spark, and modern data architectures to deliver secure, scalable, and highly available data solutions supporting Gen AI and ML applications. Responsibilities Design, build, and maintain large-scale data pipelines and distributed processing systems using Spark Develop and support data platforms that enable AI/ML and Generative AI applications Architect and implement solutions within AWS cloud environments using cloud-native patterns Build and optimize ETL/ELT workflows to support analytical and operational use cases Implement data modeling strategies aligned with modern data lakehouse architectures Ensure data quality, governance, and monitoring across all data assets Deliver secure, scalable, and highly available data solutions following operational best practices Collaborate with cross-functional teams to support AI-driven business initiatives Write advanced SQL queries and production-grade code in Java or Scala Requirements 5+ years of experience in Data Engineering with exposure to Gen AI/ML-based applications Background in building and maintaining large-scale data pipelines and distributed processing systems using Spark with Java or Scala Familiarity with AI/ML, Generative AI, or data platforms supporting AI-driven initiatives Expertise in AWS cloud environments and cloud-native architectures Advanced proficiency in SQL Skills in data modeling and modern data lakehouse architectures Understanding of data quality, governance, monitoring, and operational best practices Competency in building secure, scalable, and highly available data solutions Showcase of ETL/ELT development experience Proficiency in English at an Upper-Intermediate level (B2) or higher Nice to have Hands-on experience with Databricks or Snowflake, with the ability to work across modern cloud-based data platforms (experience with both is advantageous) Background in Financial Services

Requirements

• 5+ years of experience in Data Engineering with exposure to Gen AI/ML-based applications

• Background in building and maintaining large-scale data pipelines and distributed processing systems using Spark with Java or Scala

• Familiarity with AI/ML, Generative AI, or data platforms supporting AI-driven initiatives

• Expertise in AWS cloud environments and cloud-native architectures

• Advanced proficiency in SQL

• Skills in data modeling and modern data lakehouse architectures

• Understanding of data quality, governance, monitoring, and operational best practices

• Competency in building secure, scalable, and highly available data solutions

• Showcase of ETL/ELT development experience

• Proficiency in English at an Upper-Intermediate level (B2) or higher

Responsibilities

• Design, build, and maintain large-scale data pipelines and distributed processing systems using Spark

• Develop and support data platforms that enable AI/ML and Generative AI applications

• Architect and implement solutions within AWS cloud environments using cloud-native patterns

• Build and optimize ETL/ELT workflows to support analytical and operational use cases

• Implement data modeling strategies aligned with modern data lakehouse architectures

• Ensure data quality, governance, and monitoring across all data assets

• Deliver secure, scalable, and highly available data solutions following operational best practices

• Collaborate with cross-functional teams to support AI-driven business initiatives

• Write advanced SQL queries and production-grade code in Java or Scala

Seniority

• Senior

Nice to have

• Hands-on experience with Databricks or Snowflake, with the ability to work across modern cloud-based data platforms (experience with both is advantageous)

• Background in Financial Services