Senior Data Engineer ( Snowflake & AWS )

Senior Data Engineer ( Snowflake & AWS )

Full Time Position

Onsite at Cranbury, New Jersey

Must Have Skills

• Data lake
• Snowflake
• AWS
• Matillion Architecture
• Data source
• Data Modelling
• 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

Data Engineering & ETL/ELT Development

• 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.

Data Architecture & Modeling

• 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.

Collaboration & Continuous Improvement

• 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.

Kindly reply with your resume to Email- jnehru@nam-it.com