Job opportunity
Data Engineer
LanceSoft, Inc.
Strong hands-on experience in
Python
programming, including Pandas, NumPy, and data processing libraries.
Strong experience with
PySpark
for distributed data processing and large-scale data transformation.
Extensive knowledge of
SQL , including complex queries, joins, CTEs, window functions, and query optimization.
Hands-on experience in
ETL/ELT development , data integration, data migration, and pipeline orchestration.
Strong experience with
Snowflake , including Snowflake SQL, virtual warehouses, stages, streams, tasks, and Snowpipe.
Good understanding of relational databases such as SQL Server, Oracle, PostgreSQL, or MySQL.
Experience in data warehousing concepts, dimensional modeling, fact and dimension tables, and schema design.
Understanding of batch processing, incremental loads, data validation, and error handling.
Experience with Git, version control, debugging, and code deployment.
Preferred Skills
Experience with cloud platforms such such as AWS, Azure, or GCP.
Knowledge of AWS S3, Azure Data Lake, or similar cloud storage solutions.
Experience with Apache Spark, Databricks, Apache Airflow, or Azure Data Factory.
Understanding of CI/CD pipelines and automated deployment processes.
Exposure to Kafka, real-time data processing, and streaming architectures.
Knowledge of data governance, metadata management, and data lineage.
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Apply on source site Python
programming, including Pandas, NumPy, and data processing libraries.
Strong experience with
PySpark
for distributed data processing and large-scale data transformation.
Extensive knowledge of
SQL , including complex queries, joins, CTEs, window functions, and query optimization.
Hands-on experience in
ETL/ELT development , data integration, data migration, and pipeline orchestration.
Strong experience with
Snowflake , including Snowflake SQL, virtual warehouses, stages, streams, tasks, and Snowpipe.
Good understanding of relational databases such as SQL Server, Oracle, PostgreSQL, or MySQL.
Experience in data warehousing concepts, dimensional modeling, fact and dimension tables, and schema design.
Understanding of batch processing, incremental loads, data validation, and error handling.
Experience with Git, version control, debugging, and code deployment.
Preferred Skills
Experience with cloud platforms such such as AWS, Azure, or GCP.
Knowledge of AWS S3, Azure Data Lake, or similar cloud storage solutions.
Experience with Apache Spark, Databricks, Apache Airflow, or Azure Data Factory.
Understanding of CI/CD pipelines and automated deployment processes.
Exposure to Kafka, real-time data processing, and streaming architectures.
Knowledge of data governance, metadata management, and data lineage.
#J-18808-Ljbffr