Mindlance
Responsibilities
Design, build, and maintain scalable data pipelines using
AWS Glue
and
Databricks .
Develop and optimize ETL/ELT processes using
PySpark
and
Python .
Collaborate with data scientists, analysts, and stakeholders to enable efficient data access and transformation.
Implement and maintain data lake and warehouse solutions on
AWS
(S3, Glue Catalog, Redshift, Athena, etc.).
Ensure data quality, consistency, and reliability across systems.
Optimize performance of large-scale distributed data processing workflows.
Develop automation scripts and frameworks for data ingestion, transformation, and validation.
Follow best practices for data governance, security, and compliance.
Required Skills & Experience
5–8 years
of hands-on experience in Data Engineering.
Strong proficiency in
Python
and
PySpark
for data processing and transformation.
Expertise in
AWS services
— particularly
Glue ,
S3 ,
Lambda ,
Redshift , and
Athena .
Hands-on experience with
Databricks
for building and managing data pipelines.
Experience working with large-scale data systems and optimizing performance.
Solid understanding of data modeling, data lake architecture, and ETL design principles.
Strong problem-solving skills and ability to work independently in a fast-paced environment.
EEO: Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.
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Design, build, and maintain scalable data pipelines using
AWS Glue
and
Databricks .
Develop and optimize ETL/ELT processes using
PySpark
and
Python .
Collaborate with data scientists, analysts, and stakeholders to enable efficient data access and transformation.
Implement and maintain data lake and warehouse solutions on
AWS
(S3, Glue Catalog, Redshift, Athena, etc.).
Ensure data quality, consistency, and reliability across systems.
Optimize performance of large-scale distributed data processing workflows.
Develop automation scripts and frameworks for data ingestion, transformation, and validation.
Follow best practices for data governance, security, and compliance.
Required Skills & Experience
5–8 years
of hands-on experience in Data Engineering.
Strong proficiency in
Python
and
PySpark
for data processing and transformation.
Expertise in
AWS services
— particularly
Glue ,
S3 ,
Lambda ,
Redshift , and
Athena .
Hands-on experience with
Databricks
for building and managing data pipelines.
Experience working with large-scale data systems and optimizing performance.
Solid understanding of data modeling, data lake architecture, and ETL design principles.
Strong problem-solving skills and ability to work independently in a fast-paced environment.
EEO: Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.
#J-18808-Ljbffr