NAM Info Inc
Base Pay Range
$135,000.00/yr - $135,000.00/yr Contact
Direct message the job poster from NAM Info Inc Email: yaswanth.kolla@nam-it.com Position
Data Engineer (Snowflake & AWS)
– Onsite at Cranbury, NJ (Full Time) Must Have Skills
Snowflake AWS Matillion Architecture Python Java SQL Shell Scripting At least One full Data Migration Experience- Source TO Target About the Role
As a
Data Engineer , you will be responsible for designing and developing modern data architectures and automated ETL/ELT workflows that power analytical, operational, and AI/ML workloads. You will work with diverse data sources, large datasets, and complex transformations to deliver clean, reliable, and governed data to end users. This role demands strong technical depth in
data integration ,
pipeline design ,
data modeling , and
performance optimization
across
Snowflake
and
AWS
ecosystems. Key Responsibilities
Design, build, and maintain
end-to-end ETL/ELT data pipelines
to extract, transform, and load data from structured and unstructured sources into
Snowflake
and
AWS-based data lakes . Leverage
ETL tools
such as
AWS Glue, dbt, Informatica, Talend, Matillion, or Apache NiFi
to orchestrate data ingestion and transformation at scale. Develop
parameterized, reusable, and modular ETL components
that can handle large volumes and complex transformations efficiently. Implement
incremental data loads ,
CDC (Change Data Capture) , and
real-time streaming
integrations using
Kafka, Kinesis, or Debezium . Integrate data from multiple systems (ERP, CRM, APIs, flat files, relational DBs, and cloud services) into centralized data stores. Apply
data cleansing, deduplication, enrichment, and validation
logic to ensure accuracy and consistency across data domains. Design and implement
data lakehouse and warehouse architectures
using
AWS S3 ,
Glue , and
Snowflake . Perform
conceptual, logical, and physical data modeling
to support analytics, BI, and machine learning use cases. Develop
dimensional models (Star, Snowflake)
and
Data Vault architectures
for analytical workloads. Optimize
Snowflake
performance through partitioning, clustering, materialized views, and query tuning. Manage and document
metadata, data lineage, and schema evolution
to ensure traceability and governance. Automation, Orchestration & Monitoring
Automate data workflows using
Apache Airflow ,
AWS Step Functions , or
Glue Workflows
for scheduling and dependency management. Implement
CI/CD pipelines
for ETL code deployment, testing, and version control using
Git ,
Jenkins , or
CodePipeline . Set up
data quality validation frameworks
and automated reconciliation checks across source and target systems. Monitor pipeline performance, data freshness, and SLA adherence using tools such as
CloudWatch ,
Datadog , or
Prometheus . Data Governance, Security & Quality
Establish and maintain
data quality rules , profiling, and monitoring mechanisms. Implement
data governance and access controls
aligned with enterprise security standards (IAM, encryption, masking, auditing). Collaborate with governance and compliance teams to ensure adherence to
GDPR ,
SOC 2 , or similar frameworks. Work closely with
data architects, analysts, and scientists
to understand data requirements and deliver high‑quality datasets. Support the development of
data marts ,
semantic layers , and
domain-oriented data products . Continuously explore and adopt emerging
data integration frameworks ,
streaming architectures , and
data observability tools . Required Skills & Qualifications
Bachelor’s or Master’s degree in
Computer Science, Data Engineering, Information Systems , or a related field. 8–10 years of hands‑on experience
in
data engineering and ETL/ELT development
within cloud environments. Strong proficiency in
Snowflake
(schema design, query optimization, performance tuning, and cost management). Deep expertise in
ETL/ELT tools :
AWS Glue, dbt, Informatica, Talend, Matillion, Apache NiFi, or Azure Data Factory . Experience with
AWS data stack : S3, Redshift, Lambda, Glue, Step Functions, Kinesis, and IAM. Advanced
SQL
skills and experience in
Python ,
Scala , or
Java
for data transformation and automation. Solid understanding of
data modeling techniques
(3NF, Star Schema, Snowflake Schema, Data Vault 2.0). Familiarity with
data orchestration ,
workflow automation , and
CI/CD
in data environments. Experience implementing
data validation ,
profiling , and
quality monitoring
frameworks. Understanding of
data governance ,
metadata management , and
security practices . Bonus: Exposure to
real‑time streaming ,
CDC pipelines ,
API‑based data ingestion , or
machine learning data prep .
#J-18808-Ljbffr
$135,000.00/yr - $135,000.00/yr Contact
Direct message the job poster from NAM Info Inc Email: yaswanth.kolla@nam-it.com Position
Data Engineer (Snowflake & AWS)
– Onsite at Cranbury, NJ (Full Time) Must Have Skills
Snowflake AWS Matillion Architecture Python Java SQL Shell Scripting At least One full Data Migration Experience- Source TO Target About the Role
As a
Data Engineer , you will be responsible for designing and developing modern data architectures and automated ETL/ELT workflows that power analytical, operational, and AI/ML workloads. You will work with diverse data sources, large datasets, and complex transformations to deliver clean, reliable, and governed data to end users. This role demands strong technical depth in
data integration ,
pipeline design ,
data modeling , and
performance optimization
across
Snowflake
and
AWS
ecosystems. Key Responsibilities
Design, build, and maintain
end-to-end ETL/ELT data pipelines
to extract, transform, and load data from structured and unstructured sources into
Snowflake
and
AWS-based data lakes . Leverage
ETL tools
such as
AWS Glue, dbt, Informatica, Talend, Matillion, or Apache NiFi
to orchestrate data ingestion and transformation at scale. Develop
parameterized, reusable, and modular ETL components
that can handle large volumes and complex transformations efficiently. Implement
incremental data loads ,
CDC (Change Data Capture) , and
real-time streaming
integrations using
Kafka, Kinesis, or Debezium . Integrate data from multiple systems (ERP, CRM, APIs, flat files, relational DBs, and cloud services) into centralized data stores. Apply
data cleansing, deduplication, enrichment, and validation
logic to ensure accuracy and consistency across data domains. Design and implement
data lakehouse and warehouse architectures
using
AWS S3 ,
Glue , and
Snowflake . Perform
conceptual, logical, and physical data modeling
to support analytics, BI, and machine learning use cases. Develop
dimensional models (Star, Snowflake)
and
Data Vault architectures
for analytical workloads. Optimize
Snowflake
performance through partitioning, clustering, materialized views, and query tuning. Manage and document
metadata, data lineage, and schema evolution
to ensure traceability and governance. Automation, Orchestration & Monitoring
Automate data workflows using
Apache Airflow ,
AWS Step Functions , or
Glue Workflows
for scheduling and dependency management. Implement
CI/CD pipelines
for ETL code deployment, testing, and version control using
Git ,
Jenkins , or
CodePipeline . Set up
data quality validation frameworks
and automated reconciliation checks across source and target systems. Monitor pipeline performance, data freshness, and SLA adherence using tools such as
CloudWatch ,
Datadog , or
Prometheus . Data Governance, Security & Quality
Establish and maintain
data quality rules , profiling, and monitoring mechanisms. Implement
data governance and access controls
aligned with enterprise security standards (IAM, encryption, masking, auditing). Collaborate with governance and compliance teams to ensure adherence to
GDPR ,
SOC 2 , or similar frameworks. Work closely with
data architects, analysts, and scientists
to understand data requirements and deliver high‑quality datasets. Support the development of
data marts ,
semantic layers , and
domain-oriented data products . Continuously explore and adopt emerging
data integration frameworks ,
streaming architectures , and
data observability tools . Required Skills & Qualifications
Bachelor’s or Master’s degree in
Computer Science, Data Engineering, Information Systems , or a related field. 8–10 years of hands‑on experience
in
data engineering and ETL/ELT development
within cloud environments. Strong proficiency in
Snowflake
(schema design, query optimization, performance tuning, and cost management). Deep expertise in
ETL/ELT tools :
AWS Glue, dbt, Informatica, Talend, Matillion, Apache NiFi, or Azure Data Factory . Experience with
AWS data stack : S3, Redshift, Lambda, Glue, Step Functions, Kinesis, and IAM. Advanced
SQL
skills and experience in
Python ,
Scala , or
Java
for data transformation and automation. Solid understanding of
data modeling techniques
(3NF, Star Schema, Snowflake Schema, Data Vault 2.0). Familiarity with
data orchestration ,
workflow automation , and
CI/CD
in data environments. Experience implementing
data validation ,
profiling , and
quality monitoring
frameworks. Understanding of
data governance ,
metadata management , and
security practices . Bonus: Exposure to
real‑time streaming ,
CDC pipelines ,
API‑based data ingestion , or
machine learning data prep .
#J-18808-Ljbffr