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Design, develop, and maintain scalable backend systems to support data warehousing and data lake initiatives
Build and optimize ETL/ELT processes to extract, transform, and load data from various sources into centralized data repositories
Develop and implement integration solutions for seamless data exchange between systems, applications, and platforms
Collaborate with data architects, analysts, and other stakeholders to define and implement data models, schemas, and storage solutions
Ensure data quality, consistency, and security by implementing best practices and monitoring frameworks
Monitor and troubleshoot data pipelines and systems to ensure high availability and performance
Stay up-to-date with emerging technologies and trends in data engineering and integration to recommend improvements and innovations
Document technical designs, processes, and standards for the team and stakeholders
Requirements:
Bachelor’s degree in Computer Science, Engineering, or a related field
equivalent experience considered
Proven experience as a Data Engineer with 5 or more years of experience
or in a similar backend development role
Strong proficiency in programming languages such as Python, Java, or Scala
Hands-on experience with ETL/ELT tools and frameworks (e.g., Apache Airflow, Talend, Informatica, etc.)
Extensive knowledge of relational and non-relational databases (e.g., SQL, NoSQL, PostgreSQL, MongoDB)
Expertise in building and managing enterprise data warehouses (e.g., Snowflake, Amazon Redshift, Google BigQuery) and data lakes (e.g., AWS S3, Azure Data Lake)
Familiarity with cloud platforms (AWS, Azure, Google Cloud) and their data services
Experience with API integrations and data exchange protocols (e.g., REST, SOAP, JSON, XML)
Solid understanding of data governance, security, and compliance standards
Strong analytical and problem-solving skills with attention to detail
Excellent communication and collaboration abilities
Nice to have:
Certifications in cloud platforms (AWS Certified Data Analytics, Azure Data Engineer, etc.)
Experience with big data technologies (e.g., Apache Hadoop, Spark, Kafka)
Knowledge of data visualization tools (e.g., Tableau, Power BI) for supporting downstream analytics
Familiarity with DevOps practices and tools (e.g., Docker, Kubernetes, Jenkins)