Description

Key Responsibilities: 

Design, develop, and maintain scalable ETL/ELT pipelines to ingest data from multiple structured and unstructured sources.​
Architect and optimize data warehouses, data lakes, and lakehouse solutions (e.g., Snowflake, Redshift, BigQuery, Delta Lake).​
Implement robust data models (dimensional, relational) to support analytics and BI use cases.​
Ensure data quality, reliability, and governance through validation rules, monitoring, and alerting.​
Work with big data and streaming technologies such as Spark, Kafka, Flink, or Hadoop to process large-scale batch and real-time data.​
Optimize SQL and NoSQL queries, storage strategies, and performance for high-volume datasets.​
Collaborate with data scientists to productionize ML models and integrate them into data pipelines.​
Partner with product, engineering, and business teams to understand data requirements and translate them into technical solutions.​
Lead technical design reviews, enforce best practices, and mentor junior data engineers.​
Implement security, compliance, and access controls for sensitive data.​
Required Skills & Experience
5+ years of hands-on experience in data engineering or related fields.​
Strong proficiency in SQL and at least one programming language (Python, Scala, or Java).​
Experience with modern data platforms and cloud services (AWS, Azure, or GCP – e.g., S3, EMR, Databricks, BigQuery).​
Solid understanding of data modeling, ETL/ELT patterns, and data warehousing concepts.​
Practical experience with orchestration tools such as Airflow, Prefect, or Dagster.​
Familiarity with CI/CD, containerization (Docker, Kubernetes), and DevOps practices for data pipelines.​
Strong problem-solving skills and ability to troubleshoot complex data issues end-to-end.​
Excellent communication skills and experience collaborating with cross-functional teams

Key Skills

Education

Any Graduate