Create and maintain an optimal data architecture.

Collect complex data for the functional and non-functional needs of the company.

Identify, design, and implement the development process which includes process automation, optimization of data provision, redesign of scalable infrastructure, and so on.

Develop optimal infrastructure for the needs of extracting, transforming, and loading data from various sources such as SQL and AWS big data technologies.

Build a data pipeline analysis tool that can provide insights into the customer acquisition process, operational efficiency, and other metrics used to measure the company's business performance.

Work closely with company executives, product teams, and design teams to address data-related issues and support data infrastructure needs.

Perform data separation and ensure data security in various data centers.

Contribute to product development and optimization by data analysts and data scientists by creating data analysis tools.

Collaborate with data and analytics experts to create more functional data systems.

Bachelor's degree in informatics engineering, statistics, applied mathematics, and other related quantitative fields.

Have good project management and organizational skills.

Able to work with various teams within the company in a dynamic work environment.

Experienced in building and optimizing big data, data architecture and data sets.

Have strong analytical skills to work on unstructured data sets.

Have a strong understanding of using SQL and processing relational databases, query authoring, and various other database variations.

Experienced in carrying out root cause analysis on internal and external data to answer the company's business needs and identify potential developments.

Build processes to support data transformation, data structuring, metadata, and workload management.

Experienced in manipulating, processing and extracting data from a number of unconnected data sets.

Have an understanding of message queuing, stream processing, and scalable big data storage models.

Experienced using various software / tools such as:

Big data tools: Hadoop, Spark, Kafka, and so on.

SQL and NoSQL relational databases: PostGre and Cassandra.

Data pipeline and workflow management tools: Azkaban, Luigi, Airflow, and so on.

AWS cloud services: EC2, EMR, RDS, and Redshift.

Stream processing systems: Strom, Spark-Streaming, and so on.

Object-oriented programming languages: Python, Java, C++, Scala, and so on.