Description review
Lead Azure Data Engineer
Info Resume Edge · United States · back to the listing
HR standards
55/100
needs work
Title ↔ description
84/100
solid
Reads as
Data Engineer
100% confident
How others title the same work
Large employers
- Senior Software Engineer - Data Platform Coinbase
- Senior Security Data Engineer Doordash
- Data Engineer, Product Analytics Meta
- Senior Staff Data Engineer Mozilla
- Staff, Analytics Engineer, GTM Data Science & Analytics Twilio
Startups
- Senior Data Engineer Camber
- Cardog | Toronto, Canada or REMOTE | Full-time Cardog
- Connie Health | Senior Analytics Engineer | Boston, MA | HYBRID | $150k-$185k + equity Connie Health
- DoubleVerify (DV Scibids) | Sr. Data Engineer I | ONSITE/HYBRID - New York, NY (3x/week) | Full-time | $89K–$178K DoubleVerify (DV Scibids)
- Forecasting Research Institute (FRI) | Data Engineer | REMOTE | Full-time Forecasting Research Institute (FRI)
What the listing never says
- 26 bullet points. Long requirement lists deter qualified candidates, who read them as hard gates. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
The listing, marked up
Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.
Key Responsibilities
• Participate in the modernization and migration of current Azure data and analytics systems to the Databricks Lakehouse Platform.
• Support Databricks platform enablement, configuration, and deployment activities.
• Design, develop, and maintain ETL/ELT pipelines using Azure Data Factory, Azure Functions, and Databricks.
• Build scalable data integration solutions across Azure Data Lake, Azure SQL Database, Azure Storage (Blob/File), and Databricks.
• Assist in optimizing ingestion and transformation processes for performance, reliability, and cost efficiency.
• Migrate legacy data pipelines, workflows, and processing logic into Databricks notebooks or Delta pipelines.
• Work with cross-functional teams to understand business requirements and translate them into technical solutions.
• Ensure all solutions comply with Marsh MMA standards for security, governance, and data quality.
• Perform data validation, unit testing, troubleshooting, and system performance tuning.
• Document architecture, workflows, design decisions, and operational procedures.
Required Technical Skills
•
Strong hands-on experience with:
• Azure Data Factory (ADF)
• SQL / T-SQL
• Azure Functions
• Azure SQL Database
• Azure Data Lake (ADLS Gen2)
• Azure Storage (Blob / File)
• Databricks (Notebooks, Delta Lake, Spark)
Qualifications
• Bachelors degree in Computer Science, Data Engineering, Information Technology, or related field.
• Proven experience working with cloud-based data engineering solutions, preferably in Azure ecosystems.
• Experience supporting cloud migration or modernization initiatives is a plus.
• Strong understanding of ETL/ELT concepts, data modeling, and distributed data processing.
Soft Skills
• Strong analytical and problem-solving mindset.
• Excellent communication and documentation abilities.
• Ability to collaborate with cross-functional teams across technical and business domains.
• Detail-oriented with a focus on quality and compliance.
Originally posted on Himalayas
• Participate in the modernization and migration of current Azure data and analytics systems to the Databricks Lakehouse Platform.
• Support Databricks platform enablement, configuration, and deployment activities.
• Design, develop, and maintain ETL/ELT pipelines using Azure Data Factory, Azure Functions, and Databricks.
• Build scalable data integration solutions across Azure Data Lake, Azure SQL Database, Azure Storage (Blob/File), and Databricks.
• Assist in optimizing ingestion and transformation processes for performance, reliability, and cost efficiency.
• Migrate legacy data pipelines, workflows, and processing logic into Databricks notebooks or Delta pipelines.
• Work with cross-functional teams to understand business requirements and translate them into technical solutions.
• Ensure all solutions comply with Marsh MMA standards for security, governance, and data quality.
• Perform data validation, unit testing, troubleshooting, and system performance tuning.
• Document architecture, workflows, design decisions, and operational procedures.
Required Technical Skills
•
Strong hands-on experience with:
• Azure Data Factory (ADF)
• SQL / T-SQL
• Azure Functions
• Azure SQL Database
• Azure Data Lake (ADLS Gen2)
• Azure Storage (Blob / File)
• Databricks (Notebooks, Delta Lake, Spark)
Qualifications
• Bachelors degree in Computer Science, Data Engineering, Information Technology, or related field.
• Proven experience working with cloud-based data engineering solutions, preferably in Azure ecosystems.
• Experience supporting cloud migration or modernization initiatives is a plus.
• Strong understanding of ETL/ELT concepts, data modeling, and distributed data processing.
Soft Skills
• Strong analytical and problem-solving mindset.
• Excellent communication and documentation abilities.
• Ability to collaborate with cross-functional teams across technical and business domains.
• Detail-oriented with a focus on quality and compliance.
Originally posted on Himalayas