Senior Data Engineer
Budget: $250 – $750 USD
We are searching for a strong Data Engineer to join our team in helping us to build our future DWH solution to service multiple clients and regulations.
The DWH is aimed to transition from ETL-based into Hybrid real-time (streaming-based) while keeping data integrity on the highest level.
Basic day-to-day activities that are expected from any of the candidates:
• Create and maintain optimal data pipeline architecture.
• Assemble large, complex data sets that meet functional / non-functional business requirements.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and BI technologies.
• Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
• Create data tools for analysts and data scientists that will assist them in building and optimizing our product into an innovative industry leader.
Our ideal candidate would feel comfortable with the following statements:
• Experience with big data tools: Spark, Kafka, etc.
• Experience with relational SQL and NoSQL databases, including Postgres.
• Experience with data pipeline and workflow management tools: Luigi, Airflow, etc.
• Experience with cloud services: AWS, GCP, Snowflake
• Experience with stream-processing systems: Kafka, Storm, Spark-Streaming, etc.
• Experience with object-oriented/object function scripting languages: Python or Java
Skills and expertise
Apache Airflow, Apache Spark, Database Architecture, Apache Kafka, Apache NiFi, Java, Scala, ETL Pipeline, Python, Data Science, Data Engineering, SQL, Snowflake
The DWH is aimed to transition from ETL-based into Hybrid real-time (streaming-based) while keeping data integrity on the highest level.
Basic day-to-day activities that are expected from any of the candidates:
• Create and maintain optimal data pipeline architecture.
• Assemble large, complex data sets that meet functional / non-functional business requirements.
• Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, re-designing infrastructure for greater scalability, etc.
• Build the infrastructure required for optimal extraction, transformation, and loading of data from a wide variety of data sources using SQL and BI technologies.
• Build analytics tools that utilize the data pipeline to provide actionable insights into customer acquisition, operational efficiency, and other key business performance metrics.
• Create data tools for analysts and data scientists that will assist them in building and optimizing our product into an innovative industry leader.
Our ideal candidate would feel comfortable with the following statements:
• Experience with big data tools: Spark, Kafka, etc.
• Experience with relational SQL and NoSQL databases, including Postgres.
• Experience with data pipeline and workflow management tools: Luigi, Airflow, etc.
• Experience with cloud services: AWS, GCP, Snowflake
• Experience with stream-processing systems: Kafka, Storm, Spark-Streaming, etc.
• Experience with object-oriented/object function scripting languages: Python or Java
Skills and expertise
Apache Airflow, Apache Spark, Database Architecture, Apache Kafka, Apache NiFi, Java, Scala, ETL Pipeline, Python, Data Science, Data Engineering, SQL, Snowflake