Data Engineer

About the Role

We are seeking a skilled and analytical Data Engineer to design, develop, and maintain scalable data pipelines and infrastructure that support business intelligence, analytics, and data-driven decision-making. The ideal candidate will work closely with data analysts, data scientists, software engineers, and business stakeholders to ensure the availability, reliability, and quality of data across the organization. This role involves building and optimizing data integration workflows, managing data transformation processes, and developing robust data solutions using modern engineering tools and technologies. The successful candidate will have a strong foundation in SQL, Python, database management, and data pipeline development.

Key Responsibilities

• Design, develop, test, and maintain scalable data pipelines for collecting, processing, transforming, and integrating data from multiple sources.
• Build and optimize ETL/ELT workflows to support analytics, reporting, and business intelligence initiatives.
• Develop efficient SQL queries and data transformation logic to process large datasets.
• Write and maintain data engineering scripts and applications using Python or other relevant programming languages.
• Design, implement, and maintain data models, database schemas, and data warehouse structures.
• Integrate data from relational databases, APIs, cloud storage, and other enterprise data sources.
• Monitor data pipelines and workflows to ensure data accuracy, completeness, consistency, and reliability.
• Identify and resolve data quality issues, pipeline failures, performance bottlenecks, and integration errors.
• Collaborate with data analysts, data scientists, and business teams to understand data requirements and deliver reliable data solutions.
• Implement data validation, testing, monitoring, and error-handling mechanisms.
• Support the development and maintenance of cloud-based data infrastructure and data processing systems.
• Maintain technical documentation for data pipelines, data models, workflows, and system configurations.
• Follow data governance, security, access control, and organizational data management standards.
• Participate in code reviews, testing activities, and continuous improvement initiatives.

Requirements & Tech Stack

• Bachelor's degree in Computer Science, Information Technology, Data Engineering, Software Engineering, or a related technical field.
• Strong proficiency in SQL, including joins, subqueries, aggregations, and query optimization fundamentals.
• Programming experience in Python, including data processing, scripting, and automation.
• Understanding of relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle.
• Basic knowledge of data warehousing, data modeling, and database design principles.
• Understanding of ETL/ELT concepts and data integration techniques.
• Familiarity with data structures, file formats, and data processing concepts.
• Knowledge of Git or other version control systems.
• Strong analytical, problem-solving, debugging, and communication skills.
• Ability to work collaboratively with technical teams and business stakeholders.

Preferred Qualifications

• Exposure to cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
• Familiarity with data warehouses such as Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse Analytics.
• Experience or academic project exposure to Apache Spark, PySpark, Hadoop, or distributed data processing.
• Familiarity with workflow orchestration tools such as Apache Airflow, Azure Data Factory, or AWS Glue.
• Understanding of data lake architectures, lakehouse concepts, and cloud-based storage solutions.
• Exposure to streaming technologies such as Apache Kafka or Amazon Kinesis.
• Familiarity with dbt or similar data transformation tools.
• Knowledge of data quality testing, metadata management, and data governance practices.
• Understanding of CI/CD pipelines, automated testing, and Agile development methodologies.
• Experience working with structured and semi-structured data, including JSON, CSV, and Parquet.

What We Offer

• Competitive salary and comprehensive benefits package.
• Opportunities for professional growth and career advancement.
• Collaborative and innovative technology environment.
• Exposure to modern data engineering tools, cloud platforms, and big data technologies.
• Opportunities to work on enterprise-scale data pipelines and analytics infrastructure.
• Continuous learning and development opportunities.

Benefits

• Health insurance
• Parental leave
• Professional development opportunities
• Additional benefits in accordance with company policies and applicable employment terms.