Designing and implementing data processing systems using distributed frameworks like Hadoop, Spark, Snowflake, Airflow, or other similar technologies. This involves writing efficient and scalable code to process, transform, and clean large volumes of structured and unstructured data.
Building data pipelines to ingest data from various sources such as databases, APIs, or streaming platforms. Integrating and transforming data to ensure its compatibility with the target data model or format.
Designing and optimizing data storage architectures, including data lakes, data warehouses, or distributed file systems. Implementing techniques like partitioning, compression, or indexing to optimize data storage and retrieval. Identifying and resolving bottlenecks, tuning queries, and implementing caching strategies to enhance data retrieval speed and overall system efficiency.
Designing and implementing data models that support efficient data storage, retrieval, and analysis. Collaborating with data scientists and analysts to understand their requirements and provide them with well-structured and optimized data for analysis and modeling purposes.
Utilizing frameworks like Hadoop or Spark to perform distributed computing tasks, such as parallel processing, distributed data processing, or machine learning algorithms
Implementing security measures to protect sensitive data and ensuring compliance with data privacy regulations. Establishing data governance practices to maintain data integrity, quality, and consistency.
Identifying and resolving issues related to data processing, storage, or infrastructure. Monitoring system performance, identifying anomalies, and conducting root cause analysis to ensure smooth and uninterrupted data operations.
Collaborating with cross-functional teams including data scientists, analysts, and business stakeholders to understand their requirements and provide technical solutions. Communicating complex technical concepts to non-technical stakeholders in a clear and concise manner.
Independence and responsibility for delivering a solution
Ability to work under Agile and Scrum development methodologies
Qualifications:
A bachelor's or master's degree in Computer Science, Information Systems, or a related field is typically required.
Work commercial experience as a Data Engineer or a similar role.
Proficiency in programming languages such as Python, R or Scala is essential.
In-depth knowledge and experience with distributed systems and technologies, including Apache Hadoop, Spark, Hive or similar frameworks. Familiarity with cloud-based platforms like AWS, Azure, or Google Cloud is highly desirable.
Solid understanding of data processing techniques such as batch processing, real-time streaming, and data integration. Experience with data analytics tools and frameworks like Apache Kafka, Apache Flink, or Apache Storm is a plus.
Proficiency in working with relational and non-relational databases such as MSSQL, MySQL, PostgreSQL or Cassandra. Knowledge of data warehousing concepts and technologies like Redshift, Snowflake, or BigQuery is beneficial.
Good knowledge of data storage architectures, including delta lakes, data warehouses, or distributed file systems
Experience in designing and building data pipelines (ELT/ETL) for large-scale datasets. Familiarity with tools like Databricks, Apache Nifi, Apache Airflow, or Informatica is advantageous. Experience with integration of data from multiple data sources.
Nice to have knowledge of data warehousing concepts and technologies like Synapse, Redshift, Snowflake, or BigQuery. Experience with MS Fabric is a plus.
Strong understanding of distributed computing principles, including parallel processing, data partitioning, and fault-tolerance.
