Wissen Infotech
Website:
wissen.com
Job details:
Wissen Technology is Hiring for Senior Data Engineer -Snowflake
About Wissen Technology:
At Wissen Technology, we deliver niche, custom-built products that solve complex business challenges across industries worldwide. Founded in 2015, our core philosophy is built around a strong product engineering mindset—ensuring every solution is architected and delivered right the first time. Today, Wissen Technology has a global footprint with 2000+ employees across offices in the US, UK, UAE, India, and Australia. Our commitment to excellence translates into delivering 2X impact compared to traditional service providers. How do we achieve this? Through a combination of deep domain knowledge, cutting-edge technology expertise, and a relentless focus on quality. We don’t just meet expectations—we exceed them by ensuring faster time-to-market, reduced rework, and greater alignment with client objectives. We have a proven track record of building mission-critical systems across industries, including financial services, healthcare, retail, manufacturing, and more. Wissen stands apart through its unique delivery models. Our outcome-based projects ensure predictable costs and timelines, while our agile pods provide clients with the flexibility to adapt to their evolving business needs. Wissen leverages its thought leadership and technology prowess to drive superior business outcomes. Our success is powered by top-tier talent. Our mission is clear: to be the partner of choice for building world-class custom products that deliver exceptional impact—the first time, every time.
Job Summary: We are looking for an experienced Data Engineer to join our data platform team to support the migration of a legacy Data Lake architecture to a modern Lakehouse architecture. The role involves designing and building scalable data pipelines using Apache Spark, Refiner frameworks, and other AWS cloud-native data engineering tools, while integrating with Snowflake for advanced analytics and data warehousing.
The ideal candidate will have strong experience in distributed data processing, cloud data platforms, and large-scale data migration projects.
Experience: 6- 12 years
Location: Bangalore
Mode of Work: Full time
Key Responsibilities:
- Design and implement data pipelines to migrate data from existing Data Lake to Lakehouse architecture.
- Develop and optimize Spark-based ETL/ELT pipelines using Spark Refiner or similar transformation frameworks.
- Build scalable data processing workflows using AWS services such as S3, Glue, EMR, Lambda, and Step Functions.
- Integrate and manage data ingestion into Snowflake for analytics and downstream consumption.
- Perform data modelling for Lakehouse architecture (Bronze, Silver, Gold layers).
- Ensure data quality, governance, and lineage across the migration process.
- Optimize performance of Spark jobs and Snowflake queries for large-scale datasets.
- Work closely with data architects, analytics teams, and business stakeholders to ensure reliable data delivery.
- Implement CI/CD pipelines for data engineering workflows.
- Support data validation, reconciliation, and testing during migration.
Requirements:
- Strong experience with Apache Spark (PySpark / Scala)
- Experience with Spark Refiner or similar transformation frameworks
- Hands-on expertise with AWS Data Ecosystem, including:
- S3, Glue, EMR, Lambda, Step Functions, IAM
- Experience building large-scale ETL/ELT pipelines
- Strong knowledge of Snowflake, including:
- Snowpipe
- Data loading
- Performance optimization
- Excellent SQL and data modelling skills
- Understanding of Data Lake, Lakehouse architectures, and modern data storage formats
- Knowledge of data migration strategies and validation techniques
Good To Have Skills:
- Experience with Delta Lake, Iceberg, or Hudi
- Familiarity with Airflow or other workflow orchestration tools
- Knowledge of DevOps practices and CI/CD tools
- Experience with data governance and catalog tooling
- Exposure to streaming platforms such as Kafka or Kinesis
Wissen Sites:
Website: www.wissen.com
LinkedIn: https://www.linkedin.com/company/wissen-technology
Wissen Leadership: https://www.wissen.com/company/leadership-team/
Click on Apply to know more.