Senior Data Engineer

6 days ago

Hyderabad, India Worksconsultancy Full time

Responsibilities :- Architect and implement modular, test-driven ELT pipelines using dbt on Snowflake.- Design layered data models (e.g., staging, intermediate, mart layers / medallion architecture) aligned with dbt best practices.- Lead ingestion of structured and semi-structured data from APIs, flat files, cloud storage (Azure Data Lake, AWS S3), and databases into Snowflake.- Optimize Snowflake for performance and cost : warehouse sizing, clustering, materializations, query profiling, and credit monitoring.- Apply advanced dbt capabilities including macros, packages, custom tests, sources, exposures, and documentation using dbt docs.- Orchestrate workflows using dbt Cloud, Airflow, or Azure Data Factory, integrated with CI/CD pipelines.- Define and enforce data governance and compliance practices using Snowflake RBAC, secure data sharing, and encryption strategies.- Collaborate with analysts, data scientists, architects, and business stakeholders to deliver validated, business-ready data assets.- Mentor junior engineers, lead architectural/code reviews, and help establish reusable frameworks and standards.- Engage with clients to gather requirements, present solutions, and manage end-to-end project delivery in a consulting Qualifications :- 5 to 8 years of experience in data engineering roles, with 3+ years of hands-on experience working with Snowflake and dbt in production Skills :Cloud Data Warehouse & Transformation Stack :- Expert-level knowledge of SQL and Snowflake, including performance optimization, storage layers, query profiling, clustering, and cost management.- Experience in dbt development : modular model design, macros, tests, documentation, and version control using and Integration :- Proficiency in orchestrating workflows using dbt Cloud, Airflow, or Azure Data Factory.- Comfortable working with data ingestion from cloud storage (e.g., Azure Data Lake, AWS S3) and Modelling and Architecture :- Dimensional modelling (Star/Snowflake schemas), Slowly changing dimensions.- Knowledge of modern data warehousing principles.- Experience implementing Medallion Architecture (Bronze/Silver/Gold layers).- Experience working with Parquet, JSON, CSV, or other data Languages :- Python : For data transformation, notebook development, automation.- SQL : Strong grasp of SQL for querying and performance tuning.- Jinja (nice to have) : Exposure to Jinja for advanced dbt Engineering & Analytical Skills :- ETL/ELT pipeline design and optimization.- Exposure to AI/ML data pipelines, feature stores, or MLflow for model tracking (good to have).- Exposure to data quality and validation frameworks. (ref:hirist.tech)

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