Pay: Salary not listed in the saved posting
Work setup: Unconfirmed
Employment: Unconfirmed

What you’ll work on

You’ll work closely with data scientists, analysts, and engineers to ensure the integrity, accessibility, and scalability of our data systems.
Design, develop, and maintain scalable data pipelines and ETL processes.
Build and optimize data architecture to ensure data quality and consistency.

From the employer’s posting

We are seeking a detail-oriented and highly motivated Data Engineer to join our growing Data & Analytics team. In this role, you will be responsible for designing, building, and maintaining robust data pipelines and infrastructure that power insights across the organization. You’ll work closely with data scientists, analysts, and engineers to ensure the integrity, accessibility, and scalability of our data systems. Key Responsibilities:

Key Responsibilities: Design, develop, and maintain scalable data pipelines and ETL processes. Build and optimize data architecture to ensure data quality and consistency.

Design, develop, and maintain scalable data pipelines and ETL processes. Build and optimize data architecture to ensure data quality and consistency. Integrate data from diverse internal and external sources.

Tools in this posting

Java
Python
SQL
AWS
Azure
Google Cloud (GCP)
Kafka
Redshift
Spark
Airflow
Scala
Hadoop
Snowflake
BigQuery
dbt

Source

- 5+ years of professional experience in data engineering. - Solid understanding of SQL and proficiency in at least one programming language, such as Python, Java, or Scala. - Practical experience building and maintaining data pipelines using tools like Apache Airflow or DBT.

- Practical experience building and maintaining data pipelines using tools like Apache Airflow or DBT. - Hands-on experience with cloud platforms (AWS, GCP, Azure) and data warehousing solutions (Redshift, BigQuery, Snowflake). - Familiarity with big data technologies and frameworks, including Spark, Kafka, and Hadoop.

- Hands-on experience with cloud platforms (AWS, GCP, Azure) and data warehousing solutions (Redshift, BigQuery, Snowflake). - Familiarity with big data technologies and frameworks, including Spark, Kafka, and Hadoop. - Demonstrated ability to solve complex problems with a strong focus on detail.

- Solid understanding of SQL and proficiency in at least one programming language, such as Python, Java, or Scala. - Practical experience building and maintaining data pipelines using tools like Apache Airflow or DBT. - Hands-on experience with cloud platforms (AWS, GCP, Azure) and data warehousing solutions (Redshift, BigQuery, Snowflake).

Job description

View original posting ↗

We are seeking a detail-oriented and highly motivated Data Engineer to join our growing Data & Analytics team. In this role, you will be responsible for designing, building, and maintaining robust data pipelines and infrastructure that power insights across the organization. You’ll work closely with data scientists, analysts, and engineers to ensure the integrity, accessibility, and scalability of our data systems.

Key Responsibilities:

Design, develop, and maintain scalable data pipelines and ETL processes.
Build and optimize data architecture to ensure data quality and consistency.
Integrate data from diverse internal and external sources.
Collaborate with cross-functional teams to define data requirements and deliver solutions.
Implement best practices for data governance, security, and compliance.
Monitor pipeline performance and perform real-time troubleshooting of data issues.
Participate in code reviews and contribute to documentation and standards.

Required Qualifications & Skills:

Bachelor’s degree in computer science, Engineering, or a related field (or equivalent practical experience).
5+ years of professional experience in data engineering.
Solid understanding of SQL and proficiency in at least one programming language, such as Python, Java, or Scala.
Practical experience building and maintaining data pipelines using tools like Apache Airflow or DBT.
Hands-on experience with cloud platforms (AWS, GCP, Azure) and data warehousing solutions (Redshift, BigQuery, Snowflake).
Familiarity with big data technologies and frameworks, including Spark, Kafka, and Hadoop.
Demonstrated ability to solve complex problems with a strong focus on detail.
Experience implementing CI/CD practices for data workflows.
Working knowledge of data modelling principles and schema design.
Exposure to machine learning pipelines or real-time analytics systems is a plus