We are looking for a data engineer to help modernize our data ingestion landscape and move legacy ETL processes onto MS Azure and Snowflake.
This is a hands-on engineering role for someone who wants to do more than maintain existing ETL jobs.
You will help redesign ingestion patterns, build reliable production pipelines, and create reusable components that improve how data is delivered across the organization.
Responsibilities:
Own production data ingestion solutions from design and implementation through monitoring, troubleshooting, and continuous improvement.
Modernize legacy ETL/ELT workloads using Azure and Snowflake.
Build batch, incremental, and near-real-time pipelines across databases, APIs, files, and event-based sources.
Develop workflows using Azure Data Factory and/or Synapse Pipelines.
Develop reusable Python utilities, libraries, and ingestion components.
Use Azure Databricks/Apache Spark where appropriate for data processing.
Build and support Snowflake ingestion and raw-to-curated data structures.
Improve data quality and production reliability through validation, logging, monitoring, and alerting.
Contribute to CI/CD, code reviews, security controls, and engineering standards.
Work closely with data architects, analysts, platform/application teams, and business stakeholders to turn data requirements into production solutions.
Required Skills:
3–6 years of relevant data engineering experience, or equivalent demonstrated experience.
Building and supporting production data pipelines on MS Azure.
Hands-on use of Azure Data Factory and/or Synapse Pipelines.
Practical SQL experience for building and troubleshooting data pipelines.
Python for data processing, automation, or pipeline development.
Data ingestion and data lake/lakehouse concepts.
Source control, code reviews, and deployment processes.
Diagnosing and resolving production data pipeline issues.
Communicating effectively with technical and business stakeholders.
