Data Engineer-ETL/Snowflake (S04)
Systango
Systango Technologies Limited (NSE: SYSTANGO) is a digital engineering company that offers enterprise-class IT and product engineering services to different size organizations. At Systango, we have a culture of efficiency - we use the best-in-breed technologies to commit quality at speed and world-class support to address critical business challenges. We leverage Gen AI, AI/Machine Learning and Blockchain to unlock the next stage of digitalization for traditional businesses. Our handpicked team is adept at web & enterprise development, mobile apps, QA and DevOps. Sila, Cuentas, Youtility, Porsche, MGM Grand, Deloitte, Grindr, and Tawk.to are some of the top clients that have entrusted us to enhance their digital capabilities and build disruptive innovations. We believe in making the impossible, Possible and we do it literally.
We are looking for an experienced Data Engineer with 8+ years of hands-on experience in designing, developing, and maintaining scalable data engineering solutions.
The ideal candidate should have a strong Python and Data Engineering background, with hands-on expertise in Snowflake, ETL/ELT pipelines, Databricks/PySpark, Medallion Architecture, and data quality validation.
The role requires someone who can independently understand requirements, design data solutions, build production-grade pipelines, and take ownership of delivery end-to-end.
Key Responsibilities
- Design, develop, and maintain robust ETL/ELT data pipelines for large-scale data processing.
- Develop production-grade data engineering solutions using Python, SQL, Snowflake, and Databricks/PySpark.
- Work with Medallion Architecture (Bronze, Silver, Gold layers) for building scalable and reliable data platforms.
- Design and implement data transformation, cleansing, validation, and enrichment processes.
- Implement Data Completeness, Data Consistency, Data Deduplication, and Data Quality (DCDQ) checks across data pipelines.
- Ensure data accuracy, reliability, consistency, and availability across different stages of the data pipeline.
- Work with Snowflake for data storage, transformation, optimization, and performance tuning.
- Build and optimize data processing pipelines using Databricks and PySpark.
- Develop complex SQL queries and perform data modelling for analytical workloads.
- Integrate data from multiple sources and build scalable data pipelines.
- Troubleshoot pipeline failures, data-quality issues, and performance bottlenecks.
- Collaborate with product, engineering, and business stakeholders to understand requirements and translate them into effective data solutions.
- Independently drive projects from requirement gathering → solution design → development → testing → deployment → production support.
- Follow engineering best practices around code quality, testing, monitoring, documentation, and maintainability.
Data Engineering
- 8+ years of strong hands-on experience in Data Engineering.
- Strong experience designing and developing production-grade ETL/ELT pipelines.
- Strong understanding of data pipelines, data warehousing, data modelling, and data transformation.
- Experience working with Medallion Architecture – Bronze, Silver, and Gold layers.
- Strong understanding of data quality, validation, reconciliation, and DCDQ checks.
- Strong hands-on Python programming experience.
- Advanced SQL skills.
- Hands-on experience with Apache Spark / PySpark.
- Ability to write clean, reusable, maintainable production code.
- Strong hands-on experience with Snowflake.
- Experience with Databricks and PySpark.
- Good understanding of modern data warehouse/lakehouse concepts.
- Experience with Delta Lake or similar technologies is a plus.
- Experience building and managing batch data pipelines.
- Experience with data pipeline orchestration tools such as Airflow, Databricks Workflows, or similar.
- Experience with streaming technologies such as Kafka is a plus.
- Strong understanding of data quality frameworks and validation mechanisms.
- Experience implementing checks for:
- Data completeness
- Data consistency
- Data duplication
- Data accuracy
- Data reconciliation
- Ability to identify and troubleshoot data-quality issues across different pipeline stages.
- Exposure to AWS, Azure, or GCP is good to have but not mandatory.
- Experience with CI/CD, Docker, Terraform, or Kubernetes is a plus.
- Experience with dbt is a plus.
- Strong Python-first Data Engineering background.
- 8+ years of relevant experience with significant hands-on development.
- Strong Snowflake + ETL/ELT experience.
- Good hands-on experience with Databricks/PySpark.
- Practical understanding of Medallion Architecture.
- Strong focus on data quality and DCDQ checks.
- Ability to independently own data engineering projects from requirements through production.
- Strong problem-solving and analytical skills.
- Good communication and stakeholder-management skills.
- Ability to understand business requirements, ask the right questions, and convert them into scalable technical solutions.