The Value Maximizer
Job Summary
We are seeking a skilled and motivated
Data Engineer with strong expertise in PySpark, SQL
to join our Data Engineering team. You will be responsible for designing, building, and maintaining scalable data pipelines and processing systems that support our business intelligence, analytics, and machine learning initiatives.
Key Responsibilities
Develop and maintain scalable, robust data pipelines using Scala and big data technologies
Work with large datasets from multiple sources to ingest, transform, and make data available for analytics and reporting
Collaborate with Data Scientists, Analysts, and other engineers to understand data requirements and deliver efficient solutions
Optimize ETL jobs for performance and cost
Ensure data quality, governance, and consistency across all environments
Monitor production jobs, troubleshoot issues, and ensure system reliability
Implement best practices for data engineering, including code reviews, testing, and documentation
Required Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, or related field
6+ years of experience as a Data Engineer or in a similar role
Strong skills in PySpark and SQL (experience with functional programming is a plus)
Hands-on experience with big data tools like Apache Spark, Kafka, Hadoop, or Hive
Proficiency in building ETL pipelines and working with structured and unstructured data
Experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services
Familiarity with version control systems (e.g., Git), CI/CD, and DevOps practices
Solid understanding of data warehousing and data modeling concepts
Preferred Qualifications
Knowledge of SQL and database systems such as Postgres, Redshift, or Snowflake
Exposure to containerization technologies like Docker and orchestration tools like Airflow or Kubernetes
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Data Engineer with strong expertise in PySpark, SQL
to join our Data Engineering team. You will be responsible for designing, building, and maintaining scalable data pipelines and processing systems that support our business intelligence, analytics, and machine learning initiatives.
Key Responsibilities
Develop and maintain scalable, robust data pipelines using Scala and big data technologies
Work with large datasets from multiple sources to ingest, transform, and make data available for analytics and reporting
Collaborate with Data Scientists, Analysts, and other engineers to understand data requirements and deliver efficient solutions
Optimize ETL jobs for performance and cost
Ensure data quality, governance, and consistency across all environments
Monitor production jobs, troubleshoot issues, and ensure system reliability
Implement best practices for data engineering, including code reviews, testing, and documentation
Required Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, or related field
6+ years of experience as a Data Engineer or in a similar role
Strong skills in PySpark and SQL (experience with functional programming is a plus)
Hands-on experience with big data tools like Apache Spark, Kafka, Hadoop, or Hive
Proficiency in building ETL pipelines and working with structured and unstructured data
Experience with cloud platforms (e.g., AWS, Azure, GCP) and their data services
Familiarity with version control systems (e.g., Git), CI/CD, and DevOps practices
Solid understanding of data warehousing and data modeling concepts
Preferred Qualifications
Knowledge of SQL and database systems such as Postgres, Redshift, or Snowflake
Exposure to containerization technologies like Docker and orchestration tools like Airflow or Kubernetes
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