job summary:
We are seeking an experienced Senior Data Engineer to design, develop, and optimize scalable data platforms that power enterprise analytics, machine learning, and AI-driven solutions. The ideal candidate will bring deep expertise in modern data engineering practices, cloud-native architectures, Lakehouse platforms, and distributed data processing technologies.
This role will play a critical part in building reliable, high-performance data ecosystems leveraging Databricks, Spark, Delta Lake, Snowflake, Kafka, and AWS, while also contributing to the adoption of Generative AI, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and AI-assisted engineering solutions.
Key Responsibilities
Data Platform Engineering
Design, build, and maintain scalable batch and real-time data pipelines.
Develop and optimize data ingestion, transformation, and processing frameworks for structured, semi-structured, and unstructured datasets.
Implement modern Lakehouse architectures utilizing Databricks, Delta Lake, and Medallion (Bronze, Silver, Gold) design patterns.
Build data solutions that support enterprise analytics, reporting, and machine learning initiatives.
Ensure data quality, governance, lineage, security, and compliance across data ecosystems.
Big Data & Streaming Solutions
Develop distributed data processing applications using PySpark and Spark SQL.
Build and maintain streaming pipelines using Kafka, Spark Structured Streaming, and AWS Kinesis.
Design fault-tolerant, scalable systems capable of processing large data volumes with low latency.
Optimize workload performance through partitioning strategies, clustering, caching, and query tuning.
Cloud & Lakehouse Architecture
Architect and implement cloud-based data solutions on AWS.
Utilize AWS services including S3, EMR, EC2, Athena, Redshift, RDS, Lambda, IAM, SNS, and SQS.
Design data storage and processing strategies that maximize reliability while minimizing operational costs.
Support migration initiatives from traditional Hadoop and EMR environments to modern cloud-native platforms.
Data Operations & Automation
Develop orchestration and scheduling frameworks using Airflow and Databricks Workflows.
Build CI/CD pipelines and automation frameworks for deployment, monitoring, and data platform operations.
Collaborate closely with architects, analysts, data scientists, and business stakeholders to deliver enterprise-grade solutions.
AI & Intelligent Platform Engineering
Implement Generative AI-powered solutions for engineering productivity and operational excellence.
Develop applications leveraging Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Vector Databases, and Model Context Protocol (MCP).
Build AI-assisted documentation, developer productivity tooling, and intelligent platform capabilities.
Evaluate emerging AI technologies and identify opportunities for adoption within data engineering processes.
Required Qualifications
Bachelor's or Master's degree in Computer Engineering, Computer Science, Information Systems, or a related field.
8+ years of experience in software engineering, data engineering, or big data platform development.
Strong experience designing and implementing enterprise-scale data pipelines.
Hands-on expertise with:
Python
PySpark
Spark SQL
SQL
Kafka
Databricks
Delta Lake
Snowflake
Hive
Experience building data solutions on AWS cloud platforms.
Strong understanding of distributed computing, data modeling, and large-scale data processing.
Experience with Git-based development workflows and CI/CD practices.
Excellent analytical, troubleshooting, and problem-solving skills.
Preferred Qualifications
Experience with real-time streaming architectures and event-driven systems.
Knowledge of data governance, metadata management, and data quality frameworks.
Experience with generative AI technologies including:
LLMs
RAG
Vector Databases
AI Agents
MCP integrations
Experience developing developer productivity tools and AI-assisted engineering workflows.
Exposure to enterprise supply chain, retail, healthcare, or manufacturing data domains.
AWS certifications are highly preferred.
Technical Skills
Programming Languages
Python
Java
SQL
Shell Scripting
C/C++
Big Data & Data Engineering
PySpark
Spark SQL
Hive
Databricks
Delta Lake
Snowflake
Kafka
HBase
Sqoop
Workflow & Orchestration
Apache Airflow
Databricks Workflows
Oozie
Cloud Technologies
AWS S3
EMR
EC2
Athena
Redshift
RDS
IAM
Lambda
SNS
SQS
AI & Modern Engineering
Generative AI
Large Language Models (LLMs)
Retrieval Augmented Generation (RAG)
Agentic AI Systems
Model Context Protocol (MCP)
Vector Databases
Visualization & Tools
Tableau
Git
Docker
Splunk
IntelliJ IDEA
PyCharm
Cursor
Preferred Certifications
AWS Certified Solutions Architect - Associate
AWS Certified Cloud Practitioner
Databricks Certifications (preferred)
What Success Looks Like
Deliver highly scalable and reliable data pipelines.
Improve platform performance, efficiency, and cost optimization.
Enable enterprise-wide analytics and AI initiatives through trusted data products.
Drive modernization of data platforms and adoption of cloud-native architectures.
Leverage AI technologies to enhance engineering efficiency, automation, and innovation.
Ideal Candidate Profile: A senior-level data engineer with extensive experience in Databricks, Spark, AWS, Kafka, Snowflake, and Lakehouse architectures, who is equally passionate about modern AI technologies and building intelligent data platforms for the future.
location: New York, New York
job type: Contract
salary: $70 - 75 per hour
work hours: 9am to 6pm
education: Bachelors
responsibilities:
We are seeking an experienced Senior Data Engineer to design, develop, and optimize scalable data platforms that power enterprise analytics, machine learning, and AI-driven solutions. The ideal candidate will bring deep expertise in modern data engineering practices, cloud-native architectures, Lakehouse platforms, and distributed data processing technologies.
This role will play a critical part in building reliable, high-performance data ecosystems leveraging Databricks, Spark, Delta Lake, Snowflake, Kafka, and AWS , while also contributing to the adoption of Generative AI, Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and AI-assisted engineering solutions .
Key Responsibilities
Data Platform Engineering
- Design, build, and maintain scalable batch and real-time data pipelines.
- Develop and optimize data ingestion, transformation, and processing frameworks for structured, semi-structured, and unstructured datasets.
- Implement modern Lakehouse architectures utilizing Databricks, Delta Lake, and Medallion (Bronze, Silver, Gold) design patterns.
- Build data solutions that support enterprise analytics, reporting, and machine learning initiatives.
- Ensure data quality, governance, lineage, security, and compliance across data ecosystems.
Big Data & Streaming Solutions
- Develop distributed data processing applications using PySpark and Spark SQL.
- Build and maintain streaming pipelines using Kafka, Spark Structured Streaming, and AWS Kinesis.
- Design fault-tolerant, scalable systems capable of processing large data volumes with low latency.
- Optimize workload performance through partitioning strategies, clustering, caching, and query tuning.
Cloud & Lakehouse Architecture
- Architect and implement cloud-based data solutions on AWS.
- Utilize AWS services including S3, EMR, EC2, Athena, Redshift, RDS, Lambda, IAM, SNS, and SQS.
- Design data storage and processing strategies that maximize reliability while minimizing operational costs.
- Support migration initiatives from traditional Hadoop and EMR environments to modern cloud-native platforms.
Data Operations & Automation
- Develop orchestration and
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