InterSources
Role:- Data Engineer
Location:- Plano, TX
Job Summary: We seek a skilled
Data Engineer
proficient in
Java and Python
to design, develop, and optimize data pipelines and systems. The ideal candidate will have experience with
big data technologies, cloud platforms, and database management , ensuring efficient data processing and analytics. Key Responsibilities:
Design, build, and maintain scalable
ETL pipelines
using
Python and Java . Develop and optimize
data architectures
for structured and unstructured data. Work with
big data frameworks
such as
Apache Spark, Hadoop, or Kafka . Implement
data integration, transformation, and migration
processes. Develop and maintain
RESTful APIs
to facilitate data access and processing. Optimize
database performance
in SQL and NoSQL environments. Collaborate with
data scientists, analysts, and software engineers
to support data-driven decision-making. Ensure
data quality, governance, and security
across the organization. Deploy and monitor data workflows in
cloud environments
(AWS, GCP, or Azure). Automate
data processing tasks
using scheduling tools like
Apache Airflow
or
Luigi .
Job Summary: We seek a skilled
Data Engineer
proficient in
Java and Python
to design, develop, and optimize data pipelines and systems. The ideal candidate will have experience with
big data technologies, cloud platforms, and database management , ensuring efficient data processing and analytics. Key Responsibilities:
Design, build, and maintain scalable
ETL pipelines
using
Python and Java . Develop and optimize
data architectures
for structured and unstructured data. Work with
big data frameworks
such as
Apache Spark, Hadoop, or Kafka . Implement
data integration, transformation, and migration
processes. Develop and maintain
RESTful APIs
to facilitate data access and processing. Optimize
database performance
in SQL and NoSQL environments. Collaborate with
data scientists, analysts, and software engineers
to support data-driven decision-making. Ensure
data quality, governance, and security
across the organization. Deploy and monitor data workflows in
cloud environments
(AWS, GCP, or Azure). Automate
data processing tasks
using scheduling tools like
Apache Airflow
or
Luigi .