Senior Data Engineer – 8+ Years Experience
hudsonmanpower
New Jersey, New Jersey, United States · New Jersey · United States
Posted Aug 13, 2026
- Contract
- On-site
- Information Technology
- mid_level
hudsonmanpower
New Jersey, New Jersey, United States · New Jersey · United States
Posted Aug 13, 2026
Job Summary
We are looking for an experienced Senior Data Engineer with 8+ years of hands-on experience in designing, developing, and maintaining scalable data platforms, data pipelines, and analytics solutions. The ideal candidate will have strong expertise in Python/SQL, ETL/ELT, cloud data platforms, data warehousing, distributed data processing, orchestration, and data architecture.
The candidate will work closely with Data Scientists, BI Developers, Software Engineers, Product Managers, and business stakeholders to build reliable, secure, high-performance data solutions that support business-critical analytics and AI/ML initiatives.
Key Responsibilities
Design, develop, and maintain scalable and reliable batch and real-time data pipelines.
Build robust ETL/ELT workflows to ingest, transform, validate, and distribute data from multiple sources.
Develop highly optimized and complex SQL queries, stored procedures, and data transformations.
Design and implement data warehouses, data lakes, lakehouses, and dimensional data models.
Work with large datasets using distributed processing technologies such as Apache Spark/PySpark.
Develop data pipelines using orchestration tools such as Apache Airflow, Azure Data Factory, AWS Glue, or similar platforms.
Implement data solutions on major cloud platforms such as AWS, Azure, or GCP.
Design and optimize cloud data platforms and services such as Amazon Redshift, Snowflake, Databricks, Azure Synapse, BigQuery, or equivalent technologies.
Implement data quality, data validation, reconciliation, monitoring, and observability frameworks.
Develop solutions for incremental data processing, CDC, slowly changing dimensions, partitioning, and performance optimization.
Build and maintain real-time/streaming data pipelines using technologies such as Kafka, Kinesis, or equivalent tools.
Implement appropriate data security, governance, access control, encryption, and compliance practices.
Collaborate with data architects to translate business requirements into scalable technical solutions.
Perform performance tuning of data pipelines, databases, Spark jobs, and cloud data workloads.
Establish and maintain CI/CD practices for data engineering workflows.
Write unit, integration, and data-quality tests to ensure reliability of production pipelines.
Troubleshoot production data issues and participate in incident resolution and root-cause analysis.
Conduct code reviews and promote engineering best practices across the data engineering team.
Mentor junior and mid-level data engineers and provide technical leadership.
Document data architecture, pipeline designs, data models, operational procedures, and technical decisions.
Stay current with emerging technologies in cloud, big data, data engineering, data platforms, and AI/ML.
Required Technical Skills
Programming & Database
Strong proficiency in Python.
Advanced SQL skills.
Experience with relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle.
Experience with NoSQL databases such as MongoDB, DynamoDB, Cassandra, or similar is advantageous.
Strong understanding of database design, indexing, query optimization, and transaction management.
Big Data & Distributed Processing
Strong experience with Apache Spark / PySpark.
Experience with Hadoop ecosystem technologies is desirable.
Understanding of distributed computing, partitioning, parallel processing, and performance optimization.
Data Engineering & ETL
Extensive experience building ETL/ELT pipelines.
Experience with tools such as:
Apache Airflow
Azure Data Factory
AWS Glue
dbt
Informatica
Talend
SSIS
Experience handling structured, semi-structured, and unstructured data.
Cloud Technologies
Strong experience with at least one major cloud platform:
AWS
S3
Glue
EMR
Redshift
Lambda
Kinesis
Athena
IAM
Azure
Azure Data Factory
Azure Data Lake Storage
Azure Databricks
Azure Synapse Analytics
Azure Functions
Event Hubs
Key Vault
GCP
BigQuery
Cloud Storage
Dataflow
Dataproc
Pub/Sub
Cloud Composer
Data Warehousing & Lakehouse
Strong understanding of data warehouse architecture.
Experience with Snowflake, Databricks, Redshift, Synapse, BigQuery, or equivalent.
Expertise in:
Star and Snowflake schemas
Fact and dimension tables
Slowly Changing Dimensions (SCD)
Data marts
Data lakes
Lakehouse architecture
Partitioning and clustering
Data modeling
Streaming & Real-Time Data
Experience with Apache Kafka or equivalent streaming platforms.
Understanding of producers, consumers, topics, partitions, offsets, consumer groups, and schema management.
Experience developing real-time or near-real-time data processing pipelines.
DevOps & Engineering Practices
Experience with Git/GitHub/GitLab/Bitbucket.
Experience with CI/CD pipelines.
Knowledge of Docker and Kubernetes is desirable.
Experience with Infrastructure as Code tools such as Terraform is advantageous.
Familiarity with automated testing, deployment, monitoring, and observability.
Data Governance & Security
Understanding of data governance, metadata management, lineage, data cataloging, and data quality.
Experience implementing role-based access control and secure data access.
Knowledge of privacy and compliance requirements such as GDPR, CCPA, HIPAA, or equivalent regulations, depending on business requirements.
Preferred Qualifications
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.
8+ years of professional experience in Data Engineering, Big Data, or related disciplines.
Experience leading data engineering projects from requirements through production deployment.
Experience working in Agile/Scrum environments.
Experience with BI and analytics platforms such as Power BI, Tableau, Looker, or similar.
Understanding of Machine Learning data pipelines and MLOps is a plus.
Experience with modern data stack technologies such as dbt, Databricks, Snowflake, Kafka, and cloud-native services is highly desirable.
Key Competencies
Strong analytical and problem-solving skills.
Excellent understanding of data architecture and engineering principles.
Ability to translate complex business requirements into scalable technical solutions.
Strong communication and stakeholder-management skills.
Ability to work independently and collaboratively in a distributed team.
Strong ownership and accountability for production data systems.
Ability to mentor engineers and provide technical leadership.
Focus on performance, reliability, scalability, security, and maintainability.
Experience Profile
The ideal candidate should demonstrate experience with:
Enterprise-scale data platforms
High-volume data processing
Batch and streaming architectures
Cloud migration and modernization
Data warehouse and lakehouse implementations
ETL/ELT modernization
Data quality and observability
Performance and cost optimization
API and database integrations
Real-time analytics
Data governance and security
Production support and incident management
Technical leadership and mentoring