Data Engineer (Mid - Senior)

Posted 5 months agoViewed

View full description

💎 Seniority level: Senior, 3+ years

📍 Location: Philippines

🔍 Industry: Technology services

🏢 Company: Umpisa Inc.

🗣️ Languages: English

⏳ Experience: 3+ years

🪄 Skills: AWSDockerPostgreSQLPythonSoftware DevelopmentSQLAgileApache AirflowBusiness IntelligenceCloud ComputingETLGitJavaKafkaKubernetesMachine LearningMongoDBMySQLPyTorchSCRUMSnowflakeAirflowApache KafkaAzureCassandraData engineeringNosqlSparkTensorflowCommunication SkillsAnalytical SkillsCollaborationProblem SolvingAttention to detailTime ManagementDocumentationCompliance

Requirements:

Bachelor’s degree in Computer Science, Engineering, Data Science, or related field; Master’s is a plus.
3+ years of experience in data engineering or related technical field.
Proficiency in programming languages such as Python, Java, or Scala.
Strong experience with data warehousing solutions like Amazon Redshift, Google BigQuery, or Snowflake.
Expertise in ETL/ELT pipelines using tools like Apache Airflow, Kafka, or Talend.
Understanding of relational and NoSQL databases like MySQL, PostgreSQL, or MongoDB.
Experience with cloud computing platforms such as AWS, Google Cloud, or Azure.
Familiarity with containerization tools like Docker or Kubernetes is a plus.
Experience in data modeling and schema design for large-scale systems.
Knowledge of version control systems like Git.
Strong problem-solving and analytical skills.
Experience in optimizing database performance and query efficiency.
Ability to collaborate with both technical and non-technical teams.
Excellent written and verbal communication skills.
Familiarity with machine learning pipelines and frameworks is a plus.
Experience with real-time data processing systems.
Knowledge of data privacy regulations and compliance measures.
Experience working in an Agile environment.

Responsibilities:

Design, build, and maintain scalable data pipelines for data collection, processing, and storage.
Integrate data from various sources into a unified data warehouse or data lake.
Develop and optimize ETL/ELT processes for efficient data ingestion and transformation.
Implement data architecture solutions ensuring structured, accessible data.
Monitor and enforce data quality standards and governance policies.
Automate repetitive data processing tasks for efficiency and scalability.
Collaborate with data scientists and analysts to meet their data needs.
Optimize the performance of data systems, pipelines, and queries.
Document data architecture and pipeline processes.
Monitor and troubleshoot data systems to maintain reliability.

Apply