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Data Engineer Python Jobs in Connecticut (NOW HIRING)

Casual Precision

Software/Data Engineer

Shelton, CT · On-site

$114K - $137K/yr

This is a software engineering role focused on building reliable production data systems using Python, Spark, AWS, Airflow, and dbt--not dashboard development, reporting, or analyst work. You'll ...

Tiger Analytics Inc.

GenAI Data Engineer

Hartford, CT · On-site +1

$115K - $138K/yr

As a Data Engineer, you will be responsible for designing, building, and maintaining data pipelines ... Tune complex SQL queries and Python-based processing jobs to handle petabyte-scale environments ...

Tiger Analytics Inc.

GenAI Data Engineer

Hartford, CT · On-site

$115K - $138K/yr

As a Data Engineer, you will be responsible for designing, building, and maintaining data pipelines ... Tune complex SQL queries and Python-based processing jobs to handle petabyte-scale environments ...

Sarian, Inc.

Sr Data Engineer

Hartford, CT · On-site

$115K - $138K/yr

Role: Sr Data Engineer Location: Hartford, CT Onsite position Fulltime Position TCS/Hartford JD ... Proficiency in Python for data processing and automation. * Experience with Hive and other big data ...

The Hartford

GCP Data Engineer

Hartford, CT · Hybrid

$115K - $138K/yr

Proficiency in Python and SQL for data engineering, automation, API development, and scalable data processing. * Experience building and supporting enterprise data pipelines, ETL/ELT processes, or ...

The Hartford

GCP Data Engineer

Hartford, CT · Hybrid

$100K - $151K/yr

Proficiency in Python and SQL for data engineering, automation, API development, and scalable data processing. * Experience building and supporting enterprise data pipelines, ETL/ELT processes, or ...

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Data Engineer Python information

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$21.9K

$133.2K

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How much do data engineer python jobs pay per year?

As of Sep 9, 2026, the average yearly pay for data engineer python in Connecticut is $133,153.00, according to ZipRecruiter salary data. Most workers in this role earn between $105,100.00 and $156,500.00 per year, depending on experience, location, and employer.

What is a data engineer python?

A Data Engineer Python job involves designing, building, and maintaining data pipelines using Python. These professionals ensure data is collected, processed, and stored efficiently for analytics and machine learning. They work with databases, cloud platforms, and big data technologies to optimize data workflows. Strong Python skills, SQL knowledge, and experience with ETL processes are essential for this role.

What does a data engineer python do?

As a Data Engineer Python, your daily tasks will often include designing, building, and maintaining robust data pipelines to collect, process, and store large sets of structured and unstructured data. You’ll frequently work with Python to automate data workflows and ensure data quality, while also collaborating with data scientists, analysts, and other engineering teams to support shared objectives. Monitoring system performance, troubleshooting issues, and optimizing data processes for scalability and efficiency are also key parts of the role. This position offers an engaging mix of technical problem-solving and cross-functional teamwork in a dynamic data-driven environment.

What are the key skills and qualifications needed to thrive in the data engineer python position?

To thrive as a Data Engineer Python, you need strong programming skills in Python, a solid understanding of data modeling, ETL processes, and often a degree in computer science or a related field. Experience with big data tools like Apache Spark or Hadoop, cloud data platforms such as AWS or Azure, and certifications in data engineering are valuable assets. Analytical thinking, attention to detail, and effective communication are important soft skills for collaborating across teams and solving complex data challenges. These qualifications are critical for successfully designing, building, and maintaining scalable data pipelines that support organizational decision-making.

How much does a Data Engineer Python make?

A Data Engineer specializing in Python typically earns between $90,000 and $130,000 annually, depending on experience, location, and industry. Senior roles or those with expertise in big data tools like Spark or Hadoop may have higher salaries, especially in high-demand markets.

What are popular job titles related to Data Engineer Python jobs in Connecticut?

For Data Engineer Python jobs in Connecticut, the most frequently searched job titles are:

What job categories do people searching Data Engineer Python jobs in Connecticut look for?

The top searched job categories for Data Engineer Python jobs in Connecticut are:

Data Engineer Python jobs near you
Infographic showing various Data Engineer Python job openings in Connecticut as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 79% Full Time, 12% Part Time, 1% Temporary, and 6% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $133,153 per year, or $64 per hour.

Software/Data Engineer

Shelton, CT • On-site

Casual Precision
Marketing • 11 - 50 employees
Learn more about Casual Precisionexternal

$114K - $137K/yr

Other

Posted 9 days ago

Job description

Software/Data Engineer

Location: Connecticut (Hybrid)


Work Arrangement: Full-time, hybrid. You'll work from our Connecticut office several days each week, collaborating closely with senior engineers while maintaining flexibility to work remotely on other days.


About the Role

Casual Precision is hiring a Software/Data Engineer to help build and operate the Casual Precision Data Platform (CPDP). You'll work across both sides of our data platform: ingestion (bringing operational and partner data into Bronze) and transformation (building dbt models that turn Bronze into trusted Silver and Gold datasets used for attribution, analytics, and client delivery).

This is a software engineering role focused on building reliable production data systems using Python, Spark, AWS, Airflow, and dbt—not dashboard development, reporting, or analyst work. You'll independently deliver well-defined engineering work, partner with senior engineers on architecture and platform evolution, and contribute across both ingestion and transformation as priorities shift.

What You'll Do

  • Design, build, and operate AWS Glue (PySpark) ingestion jobs and Airflow DAGs that load operational and partner data using reusable, configuration-driven patterns.
  • Build and maintain dbt models that transform Bronze into trusted Silver and Gold datasets, including tests, documentation, macros, and Airflow/Cosmos orchestration.
  • Modernize legacy SQL and data pipelines by migrating priority workloads into dbt and supporting AI-assisted document ingestion workflows.
  • Own the day-to-day reliability of assigned pipelines by monitoring production health, investigating failures, reprocessing data when required, and maintaining healthy data contracts between ingestion and transformation.
  • Support our event ingestion platform (pixels/identity) by investigating Lambda and Firehose issues, implementing targeted fixes, and protecting downstream data quality.
  • Partner with Analytics, BI, and Data Science teams to ensure trusted datasets meet business needs while following platform standards, CI/CD practices, and Dev → Stage → Production deployment processes.
  • Participate in code reviews, contribute reusable engineering patterns, and continuously improve the CPDP platform.

 

Required Skills & Experience

  • 3–5 years of experience in software engineering or data engineering with a strong focus on production data systems.
  • Strong Python for production applications and data pipelines—not just notebooks.
  • Strong SQL including window functions, incremental processing, query optimization, and performance tuning.
  • Hands-on experience with Spark and AWS Glue (or equivalent distributed data processing frameworks such as EMR).
  • Production experience with dbt, including models, sources, tests, documentation, and reusable macros.
  • Experience building and operating Airflow workflows, including scheduling, retries, alerting, and deployment.
  • Experience with Redshift or another modern cloud data warehouse.
  • Working knowledge of AWS services used in data platforms, including S3, IAM, CloudWatch, and related compute and orchestration services.
  • Understanding of Docker containers and how containerized applications
  • Familiarity with modern CI/CD practices, Git workflows, automated testing, and pull request-based deployments.
  • Experience building reliable production systems with strong logging, monitoring, idempotent processing, auditing, and operational troubleshooting.
  • Comfortable reading unfamiliar code, debugging production systems, and balancing ingestion and transformation priorities.

 

Nice to Have

  • Deep AWS Glue/PySpark experience, including partitioning strategies, job tuning, and production debugging.
  • Experience with Astronomer Cosmos or advanced dbt package and CI patterns.
  • TypeScript and AWS Lambda development.
  • Experience with Amazon Bedrock or other LLM-assisted document processing pipelines.
  • Tableau or BI experience as a consumer of curated data.
  • Familiarity with advertising technology, identity, attribution, or media analytics.
  • Exposure to Terraform, GitHub Actions, or other infrastructure automation tools.

 

What Success Looks Like

Within your first year, you'll be able to:

  • Independently deliver production-ready ingestion pipelines and dbt models.
  • Own the operational health and reliability of assigned data pipelines.
  • Improve platform quality through testing, monitoring, automation, and reusable engineering patterns.
  • Participate confidently in technical design discussions and code reviews.
  • Contribute to the continued evolution of the Casual Precision Data Platform while helping mentor junior engineers as the engineering team grows.


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