3-5+ years of experience in data engineering or related roles
Extensive experience in designing and maintaining data pipelines
Strong programming expertise in Python, Java, or Scala
Expert-level SQL skills for relational and NoSQL databases
Hands-on experience with big data frameworks like Spark or Kafka
Familiarity with cloud platforms such as AWS, Azure, or GCP
Responsibilities
Design data models using star/snowflake schemas
Manage data storage systems ensuring quality and security
Collaborate with data scientists and analysts for data initiatives
Utilize workflow orchestration tools like Apache Airflow
Benefits
Opportunities for professional development and training
Flexible work hours and remote work options
Access to advanced data tools and technologies
Supportive team culture focused on collaboration
Health and wellness benefits
Full Job Description
Data Engineer typically requires 3-6 years of experience in designing, building, and optimizing big data pipelines, architectures, and data sets . Key experience includes proficiency in SQL, Python/Scala/Java, ETL processes, and cloud platforms (AWS, Azure, or GCP). Candidates should possess strong skills in data modeling, warehousing (Snowflake, Redshift), and distributed systems like Spark or Kafka.
Core Experience & Qualifications
Technical Proficiency: 3-5+ years of experience in data engineering, data warehousing, or software engineering roles.
Data Pipelines (ETL/ELT): Extensive experience in designing, building, and maintaining robust, scalable data pipelines.
Languages: Strong programming skills in Python, Java, or Scala for data processing and automation.
SQL & Database Management: Expert-level SQL skills for querying and manipulating data in relational (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra) databases.
Big Data Technologies: Hands-on experience with Apache Spark, Hadoop, Kafka, or similar frameworks for large-scale data processing.
Cloud Platforms: Experience with cloud services such as AWS (S3, EMR, Redshift), Azure (Data Factory, Data Lake), or GCP.
Key Responsibilities & Competencies
Data Modeling & Warehousing: Designing efficient, secure data models (star/snowflake schemas).
Data Infrastructure: Managing data storage systems and ensuring data quality and security.
Collaboration: Working with data scientists and analysts to support data-driven initiatives.
Tools: Familiarity with workflow orchestration tools like Apache Airflow.
Education & Education Background
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related quantitative field.
Preferred Skills
Experience with containerization tools (Docker, Kubernetes).
Knowledge of data visualization tools (Tableau, Power BI).
Certifications in cloud platforms (AWS, GCP) or big data technologies.
