5+ Years of Experience in data engineering and big data at large scales. - Must
Extensive experience with data lakes, warehouses, and lakehouses (Snowflake, Delta Lake, Iceberg, Big Query, Redshift). - Must
Hands-on experience with Kafka, RabbitMQ, or similar for real-time data processing. - Must
Strong programming skills in Python or at least one OOP language (e.g., Java, Scala). - Must
Experience in working with any of the major cloud providers: Azure, Google Cloud , AWS) – Must
Hands-on experience in designing, building, and maintaining batch and streaming data pipelines using Python/PySpark for large-scale workloads. - Must
Familiar with the concepts of containerization and orchestration, with proficiency in Docker and Kubernetes. - Must
SW development background - Big Advantage
Expertise in ETL development, data modeling, and data warehousing best practices. - Big Advantage
Knowledge of monitoring & observability (Datadog, Prometheus, ELK, etc) - Big Advantage
Experience in working with transactional data lake formats like Delta Lake and Iceberg, including schema evolution, partitioning strategies, and performance tuning. - Big Advantage
Proven experience in developing data-driven microservices for processing and managing data. - Big Advantage
Experience with infrastructure as code, deployment automation, and CI/CD - Big Advantage
practices using tools such as Helm, ArgoCD, Terraform, GitHub Actions, and Jenkins. - Big Advantage
Experience in Design Patterns concepts –Advantage


Our stack: Azure, GCP, Databricks, Snowflake, Airflow, Spark, Kafka, Kubernetes, Neo4J, AeroSpike, ELK, DataDog, , Micro-Services, Python, SQL