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Applied Systems

Builds cloud software for insurance agencies

Data Engineer

Data EngineeringUpdated on Nov 5
Full-timeBengaluru, Karnataka, IndiaNo salary listedIn-person

About the Job

Requirements

  • At least 4 years of experience in Data or ETL Engineering, Data or ETL Architecture, and pipeline development, including at least 1 year working as a Google Cloud Platform developer.
  • Experience building and maintaining a scalable data warehouse on a cloud-based data platform, preferably BigQuery.
  • Experience with primary managed Google Cloud Platform data services, including Dataproc, Dataflow, and BigQuery.
  • Proficiency in SQL, dbt, and Python, including Apache Airflow and Composer, and hands-on experience with ETL tools such as Talend or Fivetran, or similar tools.
  • Proficiency in Git for version control.
  • Experience using dbt for data transformation.
  • Experience designing, building, and maintaining ETL processes, automated data quality checks, and reusable ETL components.
  • Familiarity with data lake and data warehousing concepts and data modeling techniques.
  • Familiarity with star and snowflake schemas, standardization, normalization, fact tables, and dimension tables.
  • Strong problem-solving skills and the ability to work independently and collaboratively.
  • Excellent communication skills and the ability to explain technical concepts to non-technical stakeholders.
  • A bachelor's-level degree in Computer Science, Management Information Systems, or Computer Information Systems, or equivalent experience.

Responsibilities

  • Design, develop, and operationalize robust, scalable data pipelines with automated data quality checks to support business needs, and take ownership of live data pipelines.
  • Understand and implement data life cycles, data lineage, custom metadata, and data governance.
  • Implement BigQuery SQL procedures and functions, with actionable logging.
  • Optimize ETL processes and data workflows to meet performance constraints and improve efficiency and scalability.
  • Lead the design and building of data pipelines from development through production, from data ingestion to consumption, using Google Cloud Platform services, Python, BigQuery, dbt, SQL, Apache Airflow, and Celigo.
  • Parse JSON, XML, and text using Dataflow, Python, or batch jobs.
  • Apply software best practices by leveraging reusable components.
  • Design, develop, and maintain robust, scalable data models and schemas for analytics and reporting requirements.
  • Optimize data-processing performance and ensure high availability and scalability of data systems and solutions.
  • Implement monitoring and alerting mechanisms to identify and resolve issues proactively.
  • Ensure data quality and consistency through testing and validation in development and production environments.
  • Troubleshoot and resolve data-related issues.
  • Create, update, and maintain technical documentation for data processes, pipelines, and models.
  • Stay current with industry trends and technologies to improve data engineering practices.

About the Company

Applied Systems

Builds cloud software for insurance agencies

Applied Systems builds cloud software for insurance agencies and brokers to manage client relationships, policies, and back-office work. Its flagship Applied Epic combines property and casualty insurance with employee benefits in one agency management platform. Staff use its browser interface to quote coverage, record policy details, track sales opportunities, manage accounting, and review dashboards without switching between separate systems. Applied sells its software alongside implementation services, customer support, and training that help agencies move their records and workflows onto the platform. The company was founded in 1983 and is headquartered in Chicago, Illinois.

Company size
1,001-5,000
Company stage
Acquired
Total funding
$2.5B
Headquarters
Chicago, IL
Founded
1983

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