Posted 21 July, 2026
Data Engineer (Snowflake / DBT / Python / PySpark)
Nexifyr Consulting Pvt Ltd
Bangalore,Karnataka,India,560037
Full Time
Reference: 424_763810_db_43de4748d9792e6e4a1fdf349cfb7cc1__583
Role: Data Engineer (Snowflake / DBT / Python / PySpark)
Experience: 8-12 years in data engineering / cloud data warehousing
Location: Bangalore, India (Hybrid / Onsite as required)
Employment Type: Full-time
Notice Period: Immediate to 30 days preferred
About the Role
We are looking for a hands-on Data Engineer with strong expertise in Snowflake, DBT, Python, and PySpark to design, build, and optimize scalable ELT data pipelines and cloud-based analytics platforms. You will work with enterprise clients across domains such as healthcare, manufacturing, and retail, delivering high-performance data integration, transformation, and analytics solutions on AWS.
Experience: 8-12 years in data engineering / cloud data warehousing
Location: Bangalore, India (Hybrid / Onsite as required)
Employment Type: Full-time
Notice Period: Immediate to 30 days preferred
About the Role
We are looking for a hands-on Data Engineer with strong expertise in Snowflake, DBT, Python, and PySpark to design, build, and optimize scalable ELT data pipelines and cloud-based analytics platforms. You will work with enterprise clients across domains such as healthcare, manufacturing, and retail, delivering high-performance data integration, transformation, and analytics solutions on AWS.
About organisation:
We are an Indian IT services and consulting firm based in Bengaluru. Founded in 2024, the company delivers digital transformation solutions to global B2B clients.
Core Capabilities
- Artificial Intelligence: Building AI/ML models and automated chatbots.
- Data & Cloud: Managing data engineering and cloud migrations.
- Automation: Implementing Robotic Process Automation (RPA) workflows.
- Core Focus: Specialises in AI/ML solutions, cloud platforms, data engineering, and automation.
- Target Market: Modernises operations primarily for business-to-business (B2B) clients globally.
- Footprint: Operates from India but serves customers across the US and UAE.
Key Responsibilities
•Design and implement end-to-end ELT data pipelines using Snowflake and DBT for enterprise analytics workloads.
•Develop modular, well-tested DBT models (staging, intermediate, marts) with incremental loading strategies, snapshots, and SCD Type-2 logic for historical dimensions.
•Build and maintain large-scale distributed data processing jobs using PySpark for batch and near real-time transformation workloads.
•Write production-grade Python code for data validation, reconciliation, automation, and orchestration of pipeline workflows.
•Automate cloud data ingestion using AWS S3, Snowpipe, Streams and Tasks for incremental and near real-time processing.
•Design fact and dimension tables following star and snowflake schema architecture for enterprise reporting.
•Optimize Snowflake query performance and warehouse utilization to reduce compute cost — clustering, pruning, caching, and SQL tuning.
•Implement CI/CD and version control practices (Git) for DBT projects and data pipeline code.
•Ensure data quality, testing, and documentation across all pipeline layers (DBT tests, custom validation frameworks).
•Collaborate with BI/analytics teams (Power BI) to deliver analytics-ready datasets and support data validation for dashboards.
Must-Have Skills
•DBT: Strong hands-on experience building transformation workflows — models, macros, Jinja templating, incremental models, tests, snapshots, and documentation.
•Python: Advanced scripting for data processing, automation, API integration, and validation frameworks (pandas, boto3, etc.).
•PySpark: Experience developing and tuning Spark jobs — DataFrames, partitioning, joins, window functions, and performance optimization on large datasets.
•Snowflake: Deep knowledge of architecture, virtual warehouses, Snowpipe, Streams & Tasks, Time Travel, zero-copy cloning, and performance tuning.
•SQL: Expert-level query writing, optimization, and data modeling (star/snowflake schemas, SCD handling).
•AWS: Working experience with S3, IAM, EC2, and event-driven ingestion patterns.
Good-to-Have Skills
•Experience with ETL tools such as Talend, Airflow, or similar orchestration frameworks.
•Exposure to Databricks, EMR, or Glue for Spark workloads.
•Familiarity with Power BI or other visualization tools for data validation and QA support.
•Snowflake SnowPro Core certification (or equivalent).
•Experience in healthcare, pharma, manufacturing, or supply-chain analytics domains.
Qualifications
•Bachelor's degree in Engineering, Computer Science, or a related field (B.Tech / B.E. / MCA).
• 8+ years of professional experience in data engineering, with at least 2 years on Snowflake and DBT.
What We Look For
•Strong analytical and problem-solving skills with a focus on data quality and reliability.
•Ability to work independently, own deliverables end-to-end, and communicate clearly with client stakeholders.
•Quick learner of modern cloud and data engineering technologies
•Develop modular, well-tested DBT models (staging, intermediate, marts) with incremental loading strategies, snapshots, and SCD Type-2 logic for historical dimensions.
•Build and maintain large-scale distributed data processing jobs using PySpark for batch and near real-time transformation workloads.
•Write production-grade Python code for data validation, reconciliation, automation, and orchestration of pipeline workflows.
•Automate cloud data ingestion using AWS S3, Snowpipe, Streams and Tasks for incremental and near real-time processing.
•Design fact and dimension tables following star and snowflake schema architecture for enterprise reporting.
•Optimize Snowflake query performance and warehouse utilization to reduce compute cost — clustering, pruning, caching, and SQL tuning.
•Implement CI/CD and version control practices (Git) for DBT projects and data pipeline code.
•Ensure data quality, testing, and documentation across all pipeline layers (DBT tests, custom validation frameworks).
•Collaborate with BI/analytics teams (Power BI) to deliver analytics-ready datasets and support data validation for dashboards.
Must-Have Skills
•DBT: Strong hands-on experience building transformation workflows — models, macros, Jinja templating, incremental models, tests, snapshots, and documentation.
•Python: Advanced scripting for data processing, automation, API integration, and validation frameworks (pandas, boto3, etc.).
•PySpark: Experience developing and tuning Spark jobs — DataFrames, partitioning, joins, window functions, and performance optimization on large datasets.
•Snowflake: Deep knowledge of architecture, virtual warehouses, Snowpipe, Streams & Tasks, Time Travel, zero-copy cloning, and performance tuning.
•SQL: Expert-level query writing, optimization, and data modeling (star/snowflake schemas, SCD handling).
•AWS: Working experience with S3, IAM, EC2, and event-driven ingestion patterns.
Good-to-Have Skills
•Experience with ETL tools such as Talend, Airflow, or similar orchestration frameworks.
•Exposure to Databricks, EMR, or Glue for Spark workloads.
•Familiarity with Power BI or other visualization tools for data validation and QA support.
•Snowflake SnowPro Core certification (or equivalent).
•Experience in healthcare, pharma, manufacturing, or supply-chain analytics domains.
Qualifications
•Bachelor's degree in Engineering, Computer Science, or a related field (B.Tech / B.E. / MCA).
• 8+ years of professional experience in data engineering, with at least 2 years on Snowflake and DBT.
What We Look For
•Strong analytical and problem-solving skills with a focus on data quality and reliability.
•Ability to work independently, own deliverables end-to-end, and communicate clearly with client stakeholders.
•Quick learner of modern cloud and data engineering technologies
