Summary
✨ AI‑Generated
Work as a hands-on data engineer building and maintaining scalable ELT transformation pipelines with dbt, SQL, and Snowflake. You will create layered data models, implement testing and freshness checks, optimize transformations, and contribute to Git-based development, code reviews, and CI/CD. Experience with additional cloud data platforms or data warehouse modernization is a plus.
Highlights
Hands-on opportunity to build scalable ELT pipelines and reusable data models using modern analytics engineering practices. The role includes performance optimization, automated testing, documentation, CI/CD, and collaboration with technical and business stakeholders.
Description
Role Overview
We are looking for a hands-on dbt Data Engineer / Developer to build, test, and maintain scalable ELT transformation pipelines using dbt, SQL, Snowflake, and modern cloud data platforms.
Design, develop, and maintain dbt models for staging, intermediate, and mart layers.
Build ELT pipelines using SQL, dbt, and Snowflake, including staging, transformation, and data mart layers; exposure to Databricks, BigQuery, Redshift, or Azure Synapse is an added advantage.
Implement dbt tests, documentation, source freshness checks, snapshots, and reusable macros.
Optimize SQL queries and dbt models for performance, reliability, and maintainability.
Work with analysts, business users, and senior data engineers to translate requirements into data models and transformation logic.
Support Git-based development, pull requests, code reviews, CI/CD deployments, and environment management.
Exposure to legacy data warehouse migration or modernization initiatives, preferably involving Teradata to Snowflake migration, including support for SQL conversion, data validation, reconciliation, and defect fixes.
Troubleshoot pipeline failures, data quality issues, and production defects in collaboration with platform and support teams.
Required Skills And Experience
Strong experience in data engineering, ETL/ELT development, analytics engineering, or data warehousing.
Strong hands-on experience with dbt Core or dbt Cloud.
Advanced SQL skills with experience in complex transformations and performance tuning.
Good understanding of dimensional modelling, star schema, data marts, and warehouse concepts.
Hands-on experience with Snowflake, including SQL development, warehouse usage, schemas, tables, views, access roles, and performance-aware query design.
Good understanding of Snowflake objects such as databases, schemas, virtual warehouses, stages, file formats, streams, tasks, and secure views.
Good understanding or hands-on exposure to Teradata concepts, SQL, data warehouse objects, BTEQ scripts, stored procedures, views, and migration activities from Teradata to Snowflake.
Experience supporting migration testing, source-to-target validation, record count checks, data quality checks, and comparison of migrated data between Teradata and Snowflake.
Good-to-have exposure to mainframe data sources, including COBOL copybooks, VSAM files, DB2 on z/OS, JCL, batch files, flat files, and mainframe-to-cloud data extraction patterns.
Experience with Git, branching strategies, pull requests, and code review processes.
Strong analytical, problem-solving, communication, and team collaboration skills.
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