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Entry Level Data Analytics Engineer Jobs in Connecticut

... Industrial Engineering, Data Analytics, Computer Science, Digital Transformation ๋˜๋Š” ์œ ๊ด€ ์ „๊ณต์ž ์šฐ๋Œ€ * Excel, PowerPoint, Word ๋“ฑ ๊ธฐ๋ณธ ๋ฌธ์„œ ์ž‘์„ฑ ๋ฐ ๋ฐ์ดํ„ฐ ์ •๋ฆฌ ์—ญ๋Ÿ‰ ...

Sr. Data Analyst

Windsor, CT ยท On-site

$45 - $50/hr

... Cloud, Analytics (SMAC) and DevOps. USM, a US ensured Minority Business Enterprise (MBE) is ... Job Title: Sr. Data Analyst Type: Contract Duration: 6+ months Location: Windsor, CT Rate: $45-50 ...

Data Engineer

Greenwich, CT ยท On-site

$128K - $154K/yr

Write optimized SQL queries and stored procedures for data retrieval and analysis; * Manage ... Bachelor s degree in Computer Science, Engineering, or related field

Data Engineer

Hartford, CT ยท On-site

$115K - $138K/yr

As a Data Engineer, you will contribute to the development of scalable data platforms and production-ready data pipelines that enable analytics, machine learning, and AI solutions. Working closely ...

Experience with GIS tools and spatial data analysis. * Programming and database skills (preferred for automation and data auditing). * Proficiency in Microsoft Excel. * Strong communication ...

Data governance needs to communicate frequently with users and developers, be knowledgeable about ... analytical, communication and technical writing skills Ability and comfort level researching ...

AI/ML Development Analyst

Norwalk, CT ยท On-site

$100K - $150K/yr

Collaborate with cross-functional teams including product, engineering, and data teams to integrate ... Strong analytical, problem-solving, and debugging skills. * Ability to work independently and ...

Lead Data Scientist

Plainville, CT ยท On-site

$144K - $198K/yr

Run analytics assets in production: keep all assets accurate and reliable and manage analytics operations leveraging technology * Engineer trustworthy data: turn large, messy operational data into ...

Lead Data Scientist

Manchester, CT ยท On-site

$144K - $198K/yr

Run analytics assets in production: keep all assets accurate and reliable and manage analytics operations leveraging technology * Engineer trustworthy data: turn large, messy operational data into ...

Showing results 41-60

Entry Level Data Analytics Engineer information

What are some typical projects an entry level data analytics engineer might work on during their first year?

As an Entry Level Data Analytics Engineer, you can expect to work on projects such as cleaning and organizing raw data, building basic data pipelines, and supporting senior engineers in developing dashboards or reports. You may also assist with troubleshooting data issues and automating repetitive tasks using scripts. These tasks help you gain hands-on experience with common tools and platforms, and provide exposure to teamwork and cross-functional collaboration with data analysts, business stakeholders, and IT teams.

What is an entry level data analytics engineer?

An Entry Level Data Analytics Engineer is a professional who assists in collecting, processing, and analyzing data to help organizations make informed decisions. They typically work with large datasets, utilize tools like SQL, Python, or Excel, and help build data pipelines and dashboards. At the entry level, they focus on supporting senior data engineers and analysts, learning best practices, and developing foundational technical skills. This role is ideal for recent graduates or those new to the field looking to gain experience in data engineering and analytics.

What are the key skills and qualifications needed to thrive as an entry level data analytics engineer, and why are they important?

To thrive as an Entry Level Data Analytics Engineer, you need foundational knowledge in statistics, data analysis, and programming, typically backed by a degree in computer science, mathematics, or a related field. Familiarity with tools such as SQL, Python, Excel, and data visualization platforms like Tableau or Power BI is commonly required. Strong problem-solving abilities, attention to detail, and effective communication skills help you interpret data and collaborate with cross-functional teams. These skills ensure you can extract meaningful insights from data, support business decisions, and contribute to data-driven organizational goals.
What are the most commonly searched types of Data Analytics Engineer jobs in Connecticut? The most popular types of Data Analytics Engineer jobs in Connecticut are:
What are popular job titles related to Entry Level Data Analytics Engineer jobs in Connecticut? For Entry Level Data Analytics Engineer jobs in Connecticut, the most frequently searched job titles are:
What job categories do people searching Entry Level Data Analytics Engineer jobs in Connecticut look for? The top searched job categories for Entry Level Data Analytics Engineer jobs in Connecticut are:

$115K - $138K/yr

Contractor

Re-posted 19 days ago


Job description

Job Description:
We are looking for a skilled Python Data Engineer with hands-on experience in Azure, PySpark, and Databricks, specifically within the insurance domain. This is a hybrid, long-term contract position requiring a mix of remote and onsite work in either Hartford, CT or Charlotte, NC. The ideal candidate will be responsible for building, optimizing, and maintaining data pipelines and supporting data-driven decision-making across the organization.
Key Responsibilities:
  • Design and develop scalable and robust data pipelines using PySpark and Python.
  • Leverage Azure Data Services (e.g., Azure Data Factory, Azure Data Lake, Azure Synapse) for data integration and transformation.
  • Utilize Databricks for distributed data processing, data wrangling, and advanced analytics.
  • Ensure data quality, integrity, and compliance with data governance and security policies.
  • Collaborate with cross-functional teams, including business analysts, data scientists, and application developers.
  • Participate in performance tuning, troubleshooting, and optimization of data workflows.
  • Translate business requirements into technical specifications, especially within the insurance industry context.
  • Develop and maintain documentation for data pipelines and architecture.