Senior Data Engineer — Scale AI Data Pipelines

Tiger Analytics Inc.; Dallas (TX); On-site; USD 140,000 - 190,000; Full time

Job summary

Tiger Analytics is a leading AI services firm partnering with global enterprises to redefine data-driven decision making. We are seeking a Senior Data Engineer to design, build, and maintain scalable data pipelines using Databricks, Spark, Python and Scala for large enterprise clients.

You will optimize ETL/ELT workflows, work with Hadoop ecosystems, and ensure data quality and reliability across complex datasets while collaborating with cross-functional teams to deliver robust data solutions.

Qualifications

• 8+ years of data engineering experience.
• Strong hands-on with Databricks and Apache Spark.
• Proficiency in Python and Scala for data processing.
• Experience with Hadoop and big data ecosystems.
• Experience with Control-M for batch scheduling.
• Strong ETL/ELT processes and data transformations.
• Strong SQL and data management concepts.
• Excellent analytical and collaboration skills.

Responsibilities

• Design, develop, and maintain scalable data pipelines using Databricks, Apache Spark, Python, and Scala.
• Develop and optimize ETL/ELT processes across diverse data sources.
• Work with Hadoop and its ecosystem to process large data volumes.
• Develop, troubleshoot, and optimize Spark applications for performance and reliability.
• Use Control-M to schedule, monitor, and manage batch jobs and dependencies.
• Troubleshoot data pipeline issues and ensure timely completion of workloads.
• Implement data validation and quality checks.

Skills

Databricks

Apache Spark

Python

Scala

Hadoop

Control-M

ETL/ELT

SQL

Data pipelines

Data quality

Job description

Tiger Analytics is a leading AI services firm partnering with global enterprises to redefine data-driven decision making. We are seeking a Senior Data Engineer to design, build, and maintain scalable data pipelines using Databricks, Spark, Python and Scala for large enterprise clients.

You will optimize ETL/ELT workflows, work with Hadoop ecosystems, and ensure data quality and reliability across complex datasets while collaborating with cross-functional teams to deliver robust data solutions.