Data Engineer
Experience: 4+ years.
Location: Vadodara & Pune
Skills Set- Snowflake, Power Bi, ETL, SQL, Data Pipelines
What you'll be doing:
- Develop, implement, and manage scalable Snowflake data warehouse solutions using advanced features such as materialized views, task automation, and clustering.
- Design and build real-time data pipelines from Kafka and other sources into Snowflake using Kafka Connect, Snowpipe, or custom solutions for streaming data ingestion.
- Create and optimize ETL/ELT workflows using tools like DBT, Airflow, or cloud-native solutions to ensure efficient data processing and transformation.
- Tune query performance, warehouse sizing, and pipeline efficiency by utilizing Snowflakes Query Profiling, Resource Monitors, and other diagnostic tools.
- Work closely with architects, data analysts, and data scientists to translate complex business requirements into scalable technical solutions.
- Enforce data governance and security standards, including data masking, encryption, and RBAC, to meet organizational compliance requirements.
- Continuously monitor data pipelines, address performance bottlenecks, and troubleshoot issues using monitoring frameworks such as Prometheus, Grafana, or Snowflake-native tools.
- Provide technical leadership, guidance, and code reviews for junior engineers, ensuring best practices in Snowflake and Kafka development are followed.
- Research emerging tools, frameworks, and methodologies in data engineering and integrate relevant technologies into the data stack.
What you need:
Basic Skills:
- 3+ years of hands-on experience with Snowflake data platform, including data modeling, performance tuning, and optimization.
- Strong experience with Apache Kafka for stream processing and real-time data integration.
- Proficiency in SQL and ETL/ELT processes.
- Solid understanding of cloud platforms such as AWS, Azure, or Google Cloud.
- Experience with scripting languages like Python, Shell, or similar for automation and data integration tasks.
- Familiarity with tools like dbt, Airflow, or similar orchestration platforms.
- Knowledge of data governance, security, and compliance best practices.
- Strong analytical and problem-solving skills with the ability to troubleshoot complex data issues.
- Ability to work in a collaborative team environment and communicate effectively with cross-functional teams
Responsibilities:
- Design, develop, and maintain Snowflake data warehouse solutions, leveraging advanced Snowflake features like clustering, partitioning, materialized views, and time travel to optimize performance, scalability, and data reliability.
- Architect and optimize ETL/ELT pipelines using tools such as Apache Airflow, DBT, or custom scripts, to ingest, transform, and load data into Snowflake from sources like Apache Kafka and other streaming/batch platforms.
- Work in collaboration with data architects, analysts, and data scientists to gather and translate complex business requirements into robust, scalable technical designs and implementations.
- Design and implement Apache Kafka-based real-time messaging systems to efficiently stream structured and semi-structured data into Snowflake, using Kafka Connect, KSQL, and Snow pipe for real-time ingestion.
- Monitor and resolve performance bottlenecks in queries, pipelines, and warehouse configurations using tools like Query Profile, Resource Monitors, and Task Performance Views.
- Implement automated data validation frameworks to ensure high-quality, reliable data throughout the ingestion and transformation lifecycle.
- Pipeline Monitoring and Optimization: Deploy and maintain pipeline monitoring solutions using Prometheus, Grafana, or cloud-native tools, ensuring efficient data flow, scalability, and cost-effective operations.
- Implement and enforce data governance policies, including role-based access control (RBAC), data masking, and auditing to meet compliance standards and safeguard sensitive information.
- Provide hands-on technical mentorship to junior data engineers, ensuring adherence to coding standards, design principles, and best practices in Snowflake, Kafka, and cloud data engineering.
- Stay current with advancements in Snowflake, Kafka, cloud services (AWS, Azure, GCP), and data engineering trends, and proactively apply new tools and methodologies to enhance the data platform.
Shubham Vishwakarma
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Follow CutshortShubham Vishwakarma
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