Lead SSIS / SQL Data Engineer or Data Architect hybrid in NJ.
4 positions.
Minimum 10 years of experience in Data Engineering.
Should be data designing experience.
Skill
Rate out of 10
Years of experience
Design and Development : Build flexible performant and scalable data solutions using Microsoft SQL Server SSIS (SQL Server Integration Services)
ETL and Data Pipelines :
Data Modeling and Architecture :
Performance Tuning and Optimization :
Position Details
Requirement
Creq
No of Positions
Role
Lead SSIS / SQL Data Engineer
Location
Hybrid in NJ
Type of Hire Contract / C2H
C ontract
Duration
Number of Interviews
In person interviews Yes / No
Client Interview Yes / No
No of submission (example not more than 3 resumes)
Timeline for submission
24 hours
Position requirements ( Remote / Hybrid / Fully onsite)
Hybrid in NJ
Job Description
Requirement :
Job Purpose / Summary
The Lead or Architect SQL / SSIS Data Engineer will be responsible for designing developing and maintaining robust data infrastructures ensuring the efficient transformation of raw data into actionable insights.
This role involves working closely business stakeholders and other IT teams to implement data management strategies and solutions.
Key Responsibilities
Design and Development :
Build flexible performant and scalable data solutions using Microsoft SQL Server SSIS (SQL Server Integration Services)
Create complex SQL objects such as tables stored procedures functions views indexes and triggers to facilitate efficient data manipulation and consistency.
ETL and Data Pipelines :
Develop and maintain ETL packages using SSIS to extract data from various sources (e.g. flat files XML files CSV files Access Excel) and load it into data warehouses.
Implement various transformations error handling and event handling techniques in SSIS to ensure efficient package execution.
Data Modeling and Architecture :
Design and develop conceptual logical and physical data models to support analytic data requirements and business intelligence needs.
Translate business needs into data models and database schema based on requirements gathered from business stakeholders.
Performance Tuning and Optimization :
Optimize database performance through query tuning indexing and other optimization techniques.
Ensure data consistency and integrity by implementing data governance and quality control measures.
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