Description:

The Big Data Engineer will handle massive datasets and build infrastructure for complex data analysis and machine learning at scale.
This role involves creating robust, scalable data pipelines that support data-driven decision-making.
The engineer will collaborate with data scientists, analysts, and software engineers to design, implement, and optimize big data platforms.
Key responsibilities include architecting and implementing data pipelines for ETL processes using tools like Apache Spark, Kafka, and Hadoop.
The engineer will develop and manage data storage solutions optimized for performance and cost-efficiency.
Collaboration on data strategy and integration is essential to align big data architecture with analytics goals.
The role requires implementing data quality and governance standards to ensure data accuracy and reliability.
Automation of data workflows using tools like Apache Airflow or AWS Glue is a key responsibility.
Monitoring and troubleshooting data systems to maintain optimal processing capabilities is required.
Staying updated on big data trends and technologies to integrate new techniques that promote innovation is expected.

Requirements:

Extensive experience with big data technologies such as Apache Spark, Hadoop, Kafka, and Hive is required.
Proven ability to design, build, and maintain ETL processes for massive datasets is essential.
Proficiency in programming languages like Python, Java, or Scala, along with SQL for data querying, is necessary.
Familiarity with cloud platforms such as AWS, GCP, or Azure, including their big data and storage services, is required.
A strong understanding of data quality standards and governance practices is essential.
A Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, or a related field is required.
Certifications in big data or cloud technologies are a plus.
A minimum of 5 years of experience in data engineering, with at least 3 years focusing on big data technologies, is required.
Experience in distributed systems and large-scale data storage management is necessary.
Familiarity with containerization technologies like Docker and Kubernetes is advantageous.

Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums are provided.
Competitive vacation, sick leave, and 20 paid holidays per year are offered.
Flexible work schedules and telecommuting options promote work-life balance.
Opportunities for training, certification reimbursement, and career advancement programs are available.
Access to wellness programs, including gym memberships, health screenings, and mental health resources, is provided.
Life insurance and short-term/long-term disability coverage are included.
Confidential counseling and support services through an Employee Assistance Program (EAP) are available.
Financial assistance for continuing education and professional development through tuition reimbursement is offered.
Opportunities to participate in community service and volunteer activities are encouraged.
Employee recognition programs celebrate achievements and milestones.