Data Engineer (SQL+Python+AWS)

We are looking for someone with the following skills :

1. Strong SQL and Data Modelling skills

Hands-on experience writing complex SQL for data transformation, analytics, and performance optimization, with solid understanding of data modelling principles for warehouse/lake-house environments.

2. Python / modern programming for data engineering

Proficiency in Python for building scalable ETL/ELT pipelines, data processing workflows, automation, and integration across data platforms.

3. Cloud data platform experience

Practical experience with modern cloud ecosystems such as AWS, Azure, or GCP, including managed data services, storage, compute, and orchestration patterns.

4. Modern data pipeline and orchestration tools

Experience designing and maintaining reliable batch and/or streaming pipelines using tools such as Airflow, dbt, Spark, Kafka, or equivalent modern data stack technologies.

5. Data warehouse / lakehouse architecture knowledge

Strong understanding of modern data architecture concepts including data lakes, data warehouses, lakehouses, medallion-style layering, data quality, and scalable ingestion/serving patterns.

Good to Have Skills

1. Experience with big data / distributed processing frameworks

Exposure to Spark, Databricks, Flink, or similar technologies for large-scale data transformation and processing.

2. Data governance and data quality practices

Familiarity with lineage, cataloging, schema management, observability, data testing, and governance controls.

3. CI/CD and infrastructure-as-code for data platforms

Experience with Git-based workflows, Terraform, Docker, Kubernetes, and deployment automation for data engineering solutions.

4. Streaming / real-time data experience

Knowledge of event-driven architectures and real-time ingestion/processing using Kafka, Kinesis, Pub/Sub, or similar tools.

5. Business intelligence and analytics enablement

Ability to work closely with analytics, product, and business teams to deliver curated datasets that support reporting, dashboards, and self-service analytics.

• Strong SQL and data modelling skills
• Hands-on experience writing complex SQL for data transformation, analytics, and performance optimization
• Solid understanding of data modelling principles for warehouse/lakehouse environments
• Proficiency in Python for building scalable ETL/ELT pipelines, data processing workflows, and automation
• Practical experience with modern cloud ecosystems such as AWS, Azure, or GCP (managed data services, storage, compute, orchestration)
• Experience designing and maintaining batch and/or streaming pipelines using tools such as Airflow, dbt, Spark, Kafka, or equivalent
• Strong understanding of modern data architecture: data lakes, data warehouses, lakehouses, medallion-style layering, data quality, scalable ingestion/serving patterns
• Exposure to big data / distributed processing frameworks such as Spark, Databricks, or Flink
• Familiarity with data governance and data quality practices: lineage, cataloging, schema management, observability, data testing
• Experience with Git-based workflows, Terraform, Docker, Kubernetes, and CI/CD deployment automation for data platforms
• Knowledge of event-driven architectures and real-time ingestion/processing using Kafka, Kinesis, Pub/Sub, or similar
• Ability to work with analytics, product, and business teams to deliver curated datasets for reporting, dashboards, and self-service analytics