About the Role We are looking for a skilled and detail-oriented Data Engineer with 2 or more years of hands-on experience in building, optimizing, and maintaining scalable data pipelines and data infrastructure. The ideal candidate should have strong knowledge of databases, ETL processes, cloud platforms, and modern data engineering practices.
You will work closely with product, analytics, and engineering teams to ensure reliable data flow, efficient processing, and high-quality datasets for business intelligence, reporting, and AI/ML initiatives.
Design, develop, and maintain scalable ETL and ELT pipelines.
Build and optimize data architectures, data lakes, and warehousing solutions.
Integrate data from multiple APIs, databases, and third-party systems.
Ensure data quality, consistency, security, and reliability across systems.
Develop automated workflows for data ingestion, transformation, and validation.
Work with structured and unstructured datasets at scale.
Optimize SQL queries and database performance.
Collaborate with backend, analytics, and AI teams for data-driven solutions.
Monitor pipelines and troubleshoot production issues.
Implement logging, monitoring, and alerting mechanisms for data systems.
Maintain proper technical documentation and workflow diagrams.
2 or more years of experience as a Data Engineer or in a similar role.
Strong proficiency in SQL and database design.
Experience with relational and NoSQL databases including MySQL, PostgreSQL, MongoDB, BigQuery, Redshift, and Snowflake.
Hands-on experience with ETL tools and data pipeline development.
Strong programming skills in Python, Node.js, or Java.
Experience with cloud platforms such as AWS, GCP, or Azure.
Familiarity with data orchestration tools such as Airflow, Prefect, or Dagster.
Understanding of APIs, webhooks, and real-time data processing.
Experience with Git and CI/CD workflows.
Knowledge of Docker and containerized deployments.
Good understanding of data security and governance practices.
Experience with Apache Spark, Kafka, or distributed processing systems.
Exposure to AI and ML data pipelines.
Knowledge of analytics and BI tools such as Power BI, Tableau, or Looker.
Experience working in a startup or fast-paced product environment.
Familiarity with microservices architecture.
Opportunity to work on scalable and impactful data systems.
Collaborative and growth-focused work environment.
Exposure to AI, analytics, and cloud-native technologies.
Flexible work culture and learning opportunities.
Competitive salary and performance-based growth.
