Data Engineer

We are looking for a skilled Data Engineer to design, develop, and maintain reliable data pipelines and analytical platforms that support business intelligence, product analytics, and machine learning initiatives. The role involves ensuring that data is accurate, secure, well-structured, documented, and readily available for analysis and reporting.

The candidate will be responsible for building and maintaining batch and real-time data pipelines, developing ETL/ELT workflows, implementing data transformations, and establishing data-quality checks. You will design efficient data warehouse schemas, data models, and analytical datasets while ensuring that data is organized and optimized for reporting, analytics, and downstream applications.

Strong knowledge of Python, SQL, databases, and data modelling is required, along with experience using tools such as Apache Airflow, dbt, Kafka, Spark, or similar data-engineering technologies. Familiarity with modern data platforms such as BigQuery, Snowflake, Redshift, or Databricks, along with cloud storage, orchestration tools, Git, and data observability practices, is expected.

The role also involves monitoring pipeline health, data freshness, processing failures, and overall data quality. You will identify and resolve pipeline issues, improve data-processing performance, and implement appropriate monitoring and alerting mechanisms to ensure reliable data delivery.

The candidate will work closely with data analysts, data scientists, software engineers, and other stakeholders to understand data requirements and build scalable data solutions. You will also contribute to implementing data governance practices, including data lineage, access controls, retention policies, privacy, and security.

The ideal candidate should have strong analytical and problem-solving skills, attention to data quality, and an understanding of modern data-engineering practices. You will be expected to build maintainable and scalable pipelines, document data workflows and models, and continuously improve the reliability, performance, and accessibility of the organization's data infrastructure.