Data Engineer with Synapse Analytics/Fabrics (Banking/Financial)

Job Role: Data Analytics Engineer

Location: Chicago, IL - 3 days Hybrid Onsite & 2 days remote ( NEED LOCAL CANDIDATES ONLY)

Pay Rate: $65-$70/hr. on C2C/1099 or $60/hr. on W2

Note:

Data analytics strong exp.

Financial/Banking background

Data Lakehouse and T-SQL relational

deploy ETL solutions on Azure

Project Overview / Contractor's Role:

Seeking a contractor who has extensive experience in Data Analytics Engineering. The Data Analytics Engineer will be responsible for using SQL and other programming languages to pull data from a variety sources into a central data platform. They must collaborate with team members and stakeholders to understand data requirements and use that to effectively structure datasets and develop metrics. Data Engineer should be an expert in SQL and data modeling. They must be a self-starter who is able to collaborate with team members and stakeholders, develop effective solutions, and present findings.

Experience Level: 3 – Senior

Qualifications (must haves):

Minimum of 10 years of Data Engineering Experience

Strong knowledge of Data Lakehouse and T-SQL relational/non-relational databases for data

access and Advanced Analytics

Experience in providing data integration solutions, building data pipelines and ETL flows

utilizing Synapse Analytics/Fabrics.

Develop and deploy ETL solutions on Azure

Excellent oral and written communication skills

Self-starter with analytical, organizational, and problem-solving skills

Must be highly flexible and adaptable to change

Educational/Experience Qualifications:

A College or University degree and/or relevant proven work experience is required.

Tasks & Responsibilities:

Collaborate with team members and stakeholders to understand data and metric

requirements

Pull data from a variety of sources into a central data platform

Structure datasets and calculate metrics based on requirements

Develop automated scripts to automate otherwise manual processes

Communicate concerns and data findings as they arise to the appropriate team members or stakeholders

Present data findings and solutions in a way that is easy for non-technical team members and stakeholders to understand