Description

Job Description

6-8 years of relevant experience in data engineering and related fields, demonstrating a robust background in data ingestion.

This role operates in a hybrid work model, combining both remote and on-site collaboration to achieve project goals.

Requirements

6-8 years of relevant experience in data engineering and related fields, demonstrating a strong background in data ingestion.
This role operates in a hybrid work model, combining both remote and on-site collaboration to achieve project goals.

Required Skills:

Foundational understanding of Snowflake and its functionalities for data warehousing.
Basic knowledge of data engineering principles and pipeline management techniques.
Strong hands on Experience with Databricks and Azure Data Factory for data ingestion tasks.
Academic exposure to PySpark, Python and SQL for data transformationsfor data transformation workflows.

Preferred Skills:

Basic understanding of database concepts and management practices.
Ability to use Cursor as IDE aor any other AI tool

Job responsibilities





Job Responsibilities

Reading and understanding customer requirements from Functional Design Documents (FDDs), including source systems, target schemas, and mapping instructions.

Reconciling FDD requirements against actual source datasets from APIs, SFTP files, Snowflake tables, and other data sources.

Translating reconciled requirements into production Databricks pipelines, including authoring configuration and data validation YAML files, and SQL, Python, and PySpark based mapping notebooks.

Changes and fixes to existing customer pipelines and shared platform code.

Root cause analysis and resolution of data discrepancies, pipeline failures, or validation issues.

Clear in-code documentation and inline comments for complex or non-obvious transformation logic

All work must adhere to Customer coding standards around formatting, structure, and inline documentation.

What we offer

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