Qode
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.
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.