to join our team. The ideal candidate will be responsible for designing, building, and maintaining scalable data pipelines and infrastructure to support analytics, reporting, and machine learning initiatives. This role requires strong experience in data integration, ETL development, and working with large-scale data systems.
Key Responsibilities:
Design, develop, and maintain robust ETL/ELT pipelines to process structured and unstructured data from various sources.
Build and optimize data systems and architecture for analytics and data science use cases.
Collaborate with data analysts, scientists, and stakeholders to gather requirements and deliver data solutions.
Ensure data quality, integrity, and security across all systems.
Monitor data workflows and troubleshoot issues in a timely manner.
Maintain and improve data models, schemas, and documentation.
Work with cloud platforms (AWS, GCP, or Azure) and modern data tools.
Automate data processes and implement best practices in data engineering.
Required Qualifications & Skills:
Bachelor's or Master's degree in Computer Science, Engineering, or related field.
[2-5]+ years of experience in data engineering or related roles.
Proficiency in SQL and programming languages like Python or Scala.
Hands-on experience with ETL tools and frameworks (e.g., Apache Airflow, dbt, Informatica).
Experience with cloud data platforms (e.g., AWS Redshift, Azure Data Lake, Google BigQuery).
Solid understanding of data modeling, warehousing, and distributed systems.
Familiarity with version control tools like Git and CI/CD pipelines.
Preferred Skills (Optional):
Experience with real-time data streaming technologies (Kafka, Spark Streaming).
Knowledge of data governance, data privacy, and compliance frameworks.
Exposure to containerization tools (Docker, Kubernetes) and infrastructure-as-code tools (Terraform, CloudFormation).
Job Type: Permanent
Pay: ?300,000.00 - ?800,000.00 per year
Work Location: In person
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