Data Engineer - Cloud Pipelines & Snowflake

Tata Consultancy Services; Detroit (MI); On-site; USD 70,000 - 110,000; Full time

Job summary

Tata Consultancy Services is seeking a Data Engineer to design, develop, and maintain scalable data pipelines for ingestion, transformation, and loading. You will build data models and pipelines with DBT, and support ETL/ELT processes using enterprise tools.

Responsibilities include implementing Snowflake data warehousing, leveraging AWS S3 and Lambda for serverless processing, and creating data integration workflows. Strong SQL, data modeling, and governance practices are essential.

Qualifications

• Bachelor's degree in a related field.
• 5+ years of experience in Data Engineering or related roles.
• Hands‑on experience with Snowflake, DBT, and enterprise ETL/ELT tools.
• Experience with scheduling/orchestration tools (Airflow, Control‑M, Autosys).
• Strong SQL and data warehousing concepts.

Responsibilities

• Design, develop, and maintain robust and scalable data pipelines for ingestion, transformation, and loading.
• Build and optimize data models and transformation workflows using DBT.
• Develop and support ETL/ELT processes using enterprise tools.
• Implement and manage data warehousing solutions using Snowflake.
• Utilize AWS S3 for data storage and data lake architectures.
• Design serverless data processing solutions using AWS Lambda.

Skills

Snowflake

DBT

ETL/ELT tools

Airflow

AWS S3

AWS Glue

AWS Lambda

SQL

Data warehousing concepts

Data modeling

Education

Bachelor's degree in Computer Science, Information Technology, Engineering

Tools

Airflow

Control-M

Autosys

Job description

Tata Consultancy Services is seeking a Data Engineer to design, develop, and maintain scalable data pipelines for ingestion, transformation, and loading. You will build data models and pipelines with DBT, and support ETL/ELT processes using enterprise tools.

Responsibilities include implementing Snowflake data warehousing, leveraging AWS S3 and Lambda for serverless processing, and creating data integration workflows. Strong SQL, data modeling, and governance practices are essential.