Senior Data Engineer

Job Responsibilities

• Data Integration & ETL Development:
• Architect and implement robust data integration pipelines to extract, transform, and load data from various sources (e.g.,
• databases, SaaS applications, APIs, MongoDB, and flat files) into a centralized data platform.
• Design and develop complex ETL processes to ensure data quality, consistency, and reliability.
• Optimize data transformation workflows to improve performance, scalability, and maintainability.
• Data Infrastructure & Platform Management:
• Implement and maintain data ingestion, processing, and storage solutions that support the organization’s operational
• analytics requirements.
• Ensure reliability, security, and availability of the data infrastructure through effective monitoring, alerts, troubleshooting,
• and disaster recovery planning.
• Lead the design and evolution of the data architecture across warehouses, data lakes, and real-time pipelines.
Data Governance, Processes & Team Leadership:
• Establish data engineering processes, best practices, coding standards, documentation requirements, and operational
• checklists.
• Define and build all data-related roles, responsibilities, workflows, and procedures within the organization.
• Lead data initiatives end-to-end, working closely with engineering, product, operations, and upper management teams.
• Act as the founding member of the data engineering function and take ownership of scaling and mentoring the data team
• as the company grows.
• Ensure alignment between data architecture and business needs, especially in areas like inventory, warehouse operations,
• SLA reporting, and order lifecycle tracking.

Job Qualifications

• Minimum 4+ years of experience as a Data Engineer (preferably in data-driven companies or fast-paced startups).
• Proven experience designing and implementing data integration and ETL pipelines using tools such as Apache Airflow,
• Airbyte, or cloud-based ETL solutions.
• Strong hands-on experience with data infrastructure, including data lakes, data warehouses, and real-time streaming
• platforms (e.g., Elastic, Google BigQuery, MongoDB).
• Proficiency in Python or Java, and excellent SQL skills for building efficient transformations and analytical queries.
• Experience with cloud computing platforms such as AWS and services like Lambda, EC2, S3, and IAM.
• Demonstrated ability to establish data engineering standards, processes, and documentation.
• Experience mentoring junior engineers or leading small technical initiatives is a strong plus.
• Excellent problem-solving skills, strong communication abilities, and the capability to collaborate effectively with cross-
• functional teams.