Data Engineer

Careers

Data Engineer

Job Description

As a Data Engineer at our company, you will have a crucial role in developing and maintaining our data infrastructure and pipelines. Your primary focus will be on ensuring data availability, accuracy, and accessibility for various business needs. If you are passionate about data, enjoy tackling complex challenges, and aspire to contribute to cutting-edge projects, this opportunity could be an excellent fit for you.

Responsibilities

• Extracting data from multiple social media platforms (e.g., Google, Meta, Apple) using APIs.
• Implementing business logic to transform data from the Raw Layer to the Staging/Enrichment area using tools like Spark and Scala.
• Conducting Data Validation and creating Data Documentation for UAT teams.
• Designing and scheduling airflow orchestration pipelines and related workflows.
• Crafting data pipelines to gather, process, and store diverse data from various sources.
• Enhancing data workflows for better performance, scalability, and reliability.
• Collaborating with data scientists and analysts to provide the necessary data for insights and analysis.
• Implementing best practices for data quality and data governance.
• Resolving data-related issues promptly.
• Staying informed about the latest trends and technologies in data engineering.

Requirements

• A bachelor’s degree in Computer Science, Information Technology, or a related field.
• Proven experience in a Data Engineering role or similar position.
• Strong programming skills in languages such as Python, Java, or Scala.
• Proficiency in SQL and experience with relational databases (e.g., PostgreSQL, MySQL).
• Familiarity with distributed data processing frameworks (e.g., Apache Spark, Hadoop).
• Experience with cloud-based data platforms (e.g., AWS, Azure, Google Cloud).
• Knowledge of data modeling and ETL processes.
• Excellent problem-solving and communication abilities.
• Ability to collaborate effectively in a team environment.

Nice to Have

• A master’s degree in a relevant field.
• Experience with real-time data processing technologies (e.g., Apache Kafka).
• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Knowledge of data warehousing solutions (e.g., Redshift, BigQuery).
• Understanding of data security and compliance requirements.
• Relevant certifications (e.g., AWS Certified Data Analytics, Google Cloud Professional Data Engineer).

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