Data Engineer 2

How is this team contributing to vision of Providence?

Ensure a continual and seamless service for customers regardless of which services they access.

What will you be responsible for?

Strong knowledge of data analysis, data integration.

Design, develop, and maintain scalable data pipelines for ingesting structured, semi-structured, and unstructured data.

Extract, parse, validate, and transform data from various file formats including CSV, JSON, XML, Excel, PDFs, and log files.

Build and maintain ELT/ETL workflows using Azure Data Factory, Azure Databricks, and Snowflake.

Working on Azure Services like Azure Data Factory, Azure Databricks, ADLS.

Proficient in data visualization, PowerBI, writing complex SQL queries using Synapse/Azure Databricks/Snowflake.

What would your day look like?

Work closely with teams to determine project engagement and priority, business data reporting and analysis requirements.

Create notebooks/scripts in Databricks/Snowflake or use Azure Data Factory to ingest data into system.

Design file parsing solutions to handle complex and unstructured data sources for analytics and reporting.

Work with different teams to understand APIs.

Integrating API data into our system for reporting purposes.

Gather requirements, develop, and implement solutions across all endpoint technologies in the Microsoft stack.

Support AI use cases by preparing datasets, enabling feature engineering, and integrating AI-powered data processing capabilities.

Troubleshoot data pipeline failures, performance bottlenecks, and data quality issues.

Who are we looking for? 3-5 years of experience in Data Engineering, ETL/ELT development, or Analytics Engineering. 2-4 years of hands-on experience with Azure cloud technologies.

Strong experience with Snowflake, Azure Data Factory, Azure Databricks, ADLS Gen2, and SQL.

building data ingestion frameworks for APIs, flat files, Excel, JSON, XML, PDFs, and other enterprise file formats.

Strong SQL skills with experience in query optimization, data modelling, and data warehousing concepts.

Familiarity with AI/ML concepts, GenAI use cases, vector search, embeddings, or AI-assisted data processing workflows.

with Git, Azure DevOps, CI/CD pipelines, and Agile methodologies.

Excellent communication and stakeholder management skills.