Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
Python
SQL
Azure
Databricks
Spark
PySpark
Where the posting names them
About You
You are a Senior Data Engineer passionate about building scalable, production-grade data pipelines and transforming complex data from multiple sources into reliable, business-ready datasets. You thrive when working with Azure Databricks, Python, and Spark to solve data integration challenges and support enterprise data ecosystems.
You enjoy connecting data across business platforms, optimizing data transformations, and ensuring accuracy and consistency throughout the data lifecycle. You take ownership of your work, ask the right questions early, and proactively communicate risks and blockers. You value clear documentation, technical discipline, and delivering high-quality solutions that support business needs.
- Strong hands-on experience with Azure Databricks.
- Advanced Python skills for data processing, transformation, and pipeline development.
- Solid experience with Apache Spark, including PySpark and/or Spark SQL, for processing data at scale.
You Will Be Accountable for the Following Responsibilities:
- Design, build, and maintain reliable, production-grade data pipelines using Azure Databricks and Python.
- Develop scalable data transformations using PySpark or Spark SQL to integrate data from multiple business systems.
Qualifications
- Strong proficiency in Azure Databricks, Python, and Apache Spark.
- Advanced SQL skills and solid knowledge of data warehousing and dimensional modeling.
- Advanced Python skills for data processing, transformation, and pipeline development.
- Solid experience with Apache Spark, including PySpark and/or Spark SQL, for processing data at scale.
- Advanced SQL proficiency, including complex transformations, data validation, and query performance optimization.
- Solid experience with Apache Spark, including PySpark and/or Spark SQL, for processing data at scale.
- Advanced SQL proficiency, including complex transformations, data validation, and query performance optimization.
- Strong knowledge of data warehousing and dimensional modeling, including facts, dimensions, and data marts.
Nice to have:
- Experience migrating SQL-based data workloads to Azure Databricks.
You Will Be Accountable for the Following Responsibilities:
- Design, build, and maintain reliable, production-grade data pipelines using Azure Databricks and Python.
- Develop scalable data transformations using PySpark or Spark SQL to integrate data from multiple business systems.
- Ingest and process data from APIs, relational databases, cloud storage, and SFTP sources.
- Develop and maintain data warehouse structures, dimensional models, and data marts aligned with business requirements.
- Write, optimize, and maintain SQL transformations to ensure data accuracy and processing efficiency.
- Implement data quality validations and consistency checks between source systems and target models.
- Strong proficiency in Azure Databricks, Python, and Apache Spark.
- Advanced SQL skills and solid knowledge of data warehousing and dimensional modeling.
- Proven experience developing and maintaining production data pipelines.
- 5+ years of professional experience in Data Engineering, designing, building, and maintaining production-grade data pipelines.
- Strong hands-on experience with Azure Databricks.
- Advanced Python skills for data processing, transformation, and pipeline development.
- Experience integrating data from multiple sources, including REST APIs, relational databases, cloud storage, and SFTP.
- Hands-on experience with Microsoft Azure data services for data ingestion, storage, processing, and integration.
- Experience implementing data quality checks and validating consistency between source systems and target data models.
- Proven experience developing and maintaining production data pipelines.
- Hands-on experience with Microsoft Azure data services and integrating data from multiple enterprise systems.
- Strong understanding of data quality, validation, and consistency across source and target systems.
New Data Engineer postings at the employers we follow are up 8% over the last 5 weeks compared with the 5 before.
August 17Last week
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
Median yearly pay, May 2025
$139,500
Expected change, 2025 to 2035
9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
You are a Senior Data Engineer passionate about building scalable, production-grade data pipelines and transforming complex data from multiple sources into reliable, business-ready datasets. You thrive when working with Azure Databricks, Python, and Spark to solve data integration challenges and support enterprise data ecosystems.
You enjoy connecting data across business platforms, optimizing data transformations, and ensuring accuracy and consistency throughout the data lifecycle. You take ownership of your work, ask the right questions early, and proactively communicate risks and blockers. You value clear documentation, technical discipline, and delivering high-quality solutions that support business needs.
You Bring to Applaudo the Following Competencies:
5+ years of professional experience in Data Engineering, designing, building, and maintaining production-grade data pipelines.
Strong hands-on experience with Azure Databricks.
Advanced Python skills for data processing, transformation, and pipeline development.
Solid experience with Apache Spark, including PySpark and/or Spark SQL, for processing data at scale.
Advanced SQL proficiency, including complex transformations, data validation, and query performance optimization.
Strong knowledge of data warehousing and dimensional modeling, including facts, dimensions, and data marts.
Experience integrating data from multiple sources, including REST APIs, relational databases, cloud storage, and SFTP.
Hands-on experience with Microsoft Azure data services for data ingestion, storage, processing, and integration.
Experience implementing data quality checks and validating consistency between source systems and target data models.
Ability to troubleshoot pipeline failures, investigate data discrepancies, and implement reliable solutions.
Strong problem-solving, analytical, and organizational skills.
Ability to work autonomously, take ownership, and follow established engineering practices and documentation standards.
Strong English communication skills, with the ability to collaborate effectively with a US-based team.
Nice to have:
Experience migrating SQL-based data workloads to Azure Databricks.
You Will Be Accountable for the Following Responsibilities:
Design, build, and maintain reliable, production-grade data pipelines using Azure Databricks and Python.
Develop scalable data transformations using PySpark or Spark SQL to integrate data from multiple business systems.
Ingest and process data from APIs, relational databases, cloud storage, and SFTP sources.
Integrate data from ticketing, CRM, marketing, retail, and other enterprise platforms into a centralized Data Hub.
Develop and maintain data warehouse structures, dimensional models, and data marts aligned with business requirements.
Write, optimize, and maintain SQL transformations to ensure data accuracy and processing efficiency.
Implement data quality validations and consistency checks between source systems and target models.
Build and maintain outbound data feeds for third-party systems according to defined specifications.
Investigate data discrepancies, troubleshoot pipeline issues, and implement appropriate corrective actions.
Document data pipelines, transformation logic, dependencies, and technical processes.
Collaborate with team members and stakeholders to clarify requirements, identify risks early, and deliver reliable data solutions.
Communicate progress, blockers, and potential delays proactively, proposing clear next steps to keep deliverables on track.
Qualifications
Strong proficiency in Azure Databricks, Python, and Apache Spark.
Advanced SQL skills and solid knowledge of data warehousing and dimensional modeling.
Proven experience developing and maintaining production data pipelines.
Hands-on experience with Microsoft Azure data services and integrating data from multiple enterprise systems.
Strong understanding of data quality, validation, and consistency across source and target systems.
Strong ownership, autonomy, documentation, and communication skills.
Ability to communicate effectively in English and collaborate with a US-based enterprise team.
Additional Information
About Us
We Are Engineered Different.
At Applaudo, talented people design, build, and scale meaningful, AI-powered solutions that create real business impact. As an AI-native organization, we collaborate across design, development, cloud, data, and artificial intelligence to turn ideas into scalable products that transform how companies operate, make decisions, and grow.
We are building a high-performance culture grounded in five values: Empowering Excellence, Collaborative Teamwork, Unsolicited Respect, Consistent Transparency, and Efficient Communication. These define how we work, how we support one another, and how we hold ourselves accountable.
Applaudo is a place for people who want to learn fast, take ownership, and work alongside strong teams they are proud to belong to. Joining us means being part of an organization that is evolving intentionally, investing in modern ways of working, and leading AI-native transformation at scale.