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Weekend Data Pipeline Engineer Jobs in Connecticut

Interon IT Solutions

GCP Data Engineer

Hartford, CT · On-site

$115K - $138K/yr

GCP Data Engineer Location: Richardson,Texas or Hartford,CT, (Hybrid / Onsite as required ... This role focuses on building and maintaining scalable data pipelines on Google Cloud Platform (GCP ...

Eversource

Data Engineer (Hybrid Schedule)

Berlin, CT · On-site

$102 - $113/hr

Highly motivated Data Engineer will conduct advanced analytics to increase the efficiency and ... Experience with data pipeline and data integration tools. Experience with database technologies.

Casual Precision

Software/Data Engineer

Shelton, CT · On-site

$114K - $137K/yr

Software/Data Engineer Location: Connecticut (Hybrid) Work Arrangement: Full-time, hybrid. You'll ... Modernize legacy SQL and data pipelines by migrating priority workloads into dbt and supporting AI ...

Tiger Analytics, LLC

GenAI Data Engineer

Hartford, CT · On-site

$80 - $120/hr

As a Data Engineer, you will be responsible for designing, building, and maintaining data pipelines, data integration processes, and data infrastructure. You will collaborate closely with data ...

Berkley

Data Engineer

Greenwich, CT · On-site

$128K - $154K/yr

Design, build, and maintain reliable, scalable data pipelines and data platforms, including batch ... Apply AIassisted engineering approaches, including LLMenabled tools or agents, to improve data ...

Berkley

Data Engineer

Greenwich, CT

$128K - $154K/yr

Design, build, and maintain reliable, scalable data pipelines and data platforms, including batch ... Apply AIassisted engineering approaches, including LLMenabled tools or agents, to improve data ...

Deloitte

Data Engineer II

Hartford, CT · On-site

$115K - $138K/yr

Design, build, and support scalable batch and near-real-time data pipelines on AWS. * Design robust ingestion patterns from source systems into S3 and into Snowflake. * Develop transformation layers ...

Berkley

Data Engineer

Greenwich, CT · On-site

$128K - $154K/yr

Design, build, and maintain reliable, scalable data pipelines and data platforms, including batch ... Apply AI-assisted engineering approaches, including LLM-enabled tools or agents, to improve data ...

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All JobsData Pipeline Engineer JobsWeekend Data Pipeline Engineer JobsWeekend Data Pipeline Engineer Jobs in Connecticut

Weekend Data Pipeline Engineer information

What does a weekend data pipeline engineer do?

A Weekend Data Pipeline Engineer is responsible for designing, building, and maintaining data pipelines that process and move data efficiently, specifically during weekend shifts. They ensure data flows smoothly between systems, monitor performance, troubleshoot issues, and implement improvements. Their work supports data analysts, scientists, and other stakeholders who rely on timely and accurate data delivery. This role often requires expertise in ETL tools, programming, and cloud platforms, as well as availability for on-call or shift-based work during weekends.

What are the key skills and qualifications needed to thrive as a weekend data pipeline engineer, and why are they important?

To thrive as a Weekend Data Pipeline Engineer, you need a solid background in computer science, data engineering principles, and experience building and maintaining data pipelines, often supported by a relevant degree. Proficiency with ETL tools, cloud platforms like AWS or GCP, and data processing frameworks such as Apache Spark or Airflow is typically required. Strong problem-solving skills, attention to detail, and effective communication are vital soft skills for managing off-hours incidents and collaborating remotely. These skills and qualities ensure the reliability, efficiency, and scalability of data systems during critical weekend operations.

What are the unique challenges of working as a weekend data pipeline engineer, and how can I effectively manage them?

As a Weekend Data Pipeline Engineer, you may face challenges such as working with limited real-time support, handling system issues during off-peak hours, and managing updates or maintenance with minimal disruption. Success in this role often requires strong problem-solving skills, the ability to work independently, and clear documentation for handoff to weekday teams. Establishing proactive communication channels and automating monitoring or alerting systems can also help ensure smooth weekend operations and collaboration with your colleagues.

What are the most commonly searched types of Data Pipeline Engineer jobs in Connecticut?

The most popular types of Data Pipeline Engineer jobs in Connecticut are:

What cities in Connecticut are hiring for Weekend Data Pipeline Engineer jobs?

Cities in Connecticut with the most Weekend Data Pipeline Engineer job openings:

Weekend Data Pipeline Engineer jobs near you

Data Engineering Internship (Summer 2027)

Castleton Commodities International, LLC

Stamford, CT • On-site

$122K - $146K/yr

Full-time

Re-posted 15 days ago

Job description

Application Deadline: September 1, 11:59 pm EST
Program Summary - Data Science & Technology Internship
Company Overview:
Castleton Commodities International is a leading global energy commodities merchant and infrastructure asset investor. As a trader, CCI deploys capital on a proprietary basis in the physical and financial commodity markets, providing the Company with market insights and access. As a strategic investor and developer, CCI leverages its market expertise, operations capabilities, and industry knowledge to invest in, and develop, select commodity infrastructure assets. Our strategically integrated platform has generated strong risk-adjusted returns for our investors since our formation.
Position Overview:
CCI is developing a leading-edge Data Science platform, as staying at the forefront of data management and analytics is essential to our investment strategy. We are looking for motivated and detail-oriented Data Engineering Interns to join our Global Data Science & Technology team in Houston, TX, Stamford, CT, & New York City offices. The Data Engineering Intern will work closely with our Data Science, Data Engineering and Commercial teams to build and optimize data pipelines that power our analytics, forecasting, and investment decision-making processes. This is a hands-on technical internship ideal for someone who enjoys solving real-world data challenges, especially around ingesting, scraping, and managing large datasets across the commodity markets.
Responsibilities:
  • Develop and maintain robust data ingestion pipelines from various internal and external sources, including APIs, FTP endpoints, and cloud data providers.
  • Develop data ingestion and transformation pipelines using Python and SQL, publishing Snowflake for downstream use in analytics and forecasting tools.
  • Work on data architecture and data management projects for both new and existing data sources.
  • Design and implement ETL processes to clean, normalize, and store structured and semi-structured data in Snowflake, our core relational data warehouse.
  • Analyze data pipeline performance and implement optimizations to improve efficiency and reliability.
  • Conduct data quality checks and build validation logic to identify anomalies and ensure data integrity for use by commercial trading and analytics teams.
  • Automate data workflows using Python, SQL, and orchestration tools (e.g., Airflow or similar).
  • Assist in transitioning legacy datasets and codebases into scalable, cloud-native workflows aligned with our modern data architecture.
  • Document data sources, pipeline logic, and data models to ensure maintainability and knowledge transfer.

Qualifications:
  • Currently pursuing a Bachelor's or higher degree in Computer Science, Engineering, Management Information Systems, or related technical field.
  • Expected graduation date of Winter 2027 or Spring/Summer 2028.
  • Strong programming experience in Python (preferred libraries: pandas, NumPy, SQL alchemy, etc.).
  • Strong understanding of SQL and experience querying relational databases (Snowflake a plus).
  • Exposure to or interest in cloud platforms (e.g., AWS, Azure), particularly with cloud data storage and compute.
  • Familiarity with web scraping frameworks and handling large-scale structured and unstructured data sources.

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