Jobs Companies The Hartford Staff Data Engineer - Hybrid

Sobre este puesto de Staff Data Engineer - Hybrid en The Hartford

The Hartford · Híbrido · Hartford, CT
Sr Staff Data Engineer - GE07DE

Staff Data Engineer - GE07CE

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.          

As a Senior Staff Data Engineer supporting Employee Benefits Sales, you will play a key role in shaping how Sales and Underwriting data is ingested, transformed, and delivered across the organization. You’ll work on modern, cloud‑based data platforms to ensure high‑quality, governed data products that drive operational efficiency, analytics, and decision‑making. This role combines deep technical expertise with strong partnership across Underwriting, Product and Enterprise Data teams.

The position offers a strong growth path toward Technical Leadership, with hands-on ownership of Quote and Underwriting data pipelines and opportunities to mentor and influence across teams.

This role will have a Hybrid work schedule, with the expectation of working in an office location (Hartford, CT; Chicago, IL; Columbus, OH; and Charlotte, NC) 3 days a week (Tuesday through Thursday).

Responsibilities:

  • Accountable for building small or medium-scale pipelines and data products. End-to-End solution delivery involving multiple platforms and technologies with small to medium complexity or certain sub-systems of large, complex implementations, leveraging ELT solutions to acquire, integrate, and operationalize data
  • Provides significant input to influence solution architecture
  • Build and implement capabilities for continuous integration and continuous delivery aligned with Enterprise DevOps practices.
  • Accountable for team development and influencing pipeline tool decisions
  • Accountable for data engineering practices (e.g. Source code management, branching, issue tracking, access, etc.) to be followed for the data pipeline
  • Independently review, prepare, design and integrate complex (type, quality, volume) data, correcting problems and recommend data cleansing/quality solutions
  • Provide expert documentation and operating guidance for users of all levels.
  • Document technical requirements and present complex technical concepts to audiences of varying sizes and levels.
  • Rapidly architect, design, prototype/POC, implement, and optimize Cloud/Hybrid architectures
  • Research, experiment, and utilize leading big data methodologies (AWS, Hadoop/EMR, Spark, Kafka, Snowflake) with cloud/on premise hybrid hosting solutions, on a project level
  • Implement, and test data processing pipelines, and data mining/data science algorithms on a variety of hosted settings (AWS,Client technology stacks)
  • Stay up to date on emerging data and analytics technologies, tools, techniques, and frameworks.
  • Evaluate and recommend all technology-based decisions for tools and frameworks for effective delivery
  • Support the development and implementation of project and portfolio strategy, roadmaps and implementation

Qualifications:

  • Candidates must be authorized to work in the US without company sponsorship. The company will not support the STEM OPT I-983 Training Plan endorsement for this position.
  • 8+ years of data engineering experience and best practices in Distributed systems, Data warehousing solutions SQL and NoSQL, ETL tools, CICD, Bigdata, Cloud Technologies (AWS/AZURE), Python/Spark, Data mesh and Data Lake, Data Fabric
  • Must have hands on experience in Snowflake, Oracle, Informatica Cloud/Power center, Github
  • Must have hands-on experience and knowledge on replication tools like Qlik, Informatica mass ingestion, Shareplex, open flow, DMS
  • Must be familiar with ETL using Pyspark/Python/snowflake native features
  • Must be familiar with AWS services such as EMR, S3, Lambda
  • Certifications on Cloud services such as AWS/GCP and Snowflake
  • Familiar with AI tools/tech stack

Nice to have qualifications:

  • Certifications on AI foundation
  • Hands-on experience with AWS Bedrock and Google Vertex

Compensation

The listed annualized base pay range is primarily based on analysis of similar positions in the external market. Actual base pay could vary and may be above or below the listed range based on factors including but not limited to performance, proficiency and demonstration of competencies required for the role. The base pay is just one component of The Hartford’s total compensation package for employees. Other rewards may include short-term or annual bonuses, long-term incentives, and on-the-spot recognition. The annualized base pay range for this role is:

$135,040 - $202,560

The posted salary range reflects our ability to hire at different position titles and levels depending on background and experience.

Equal Opportunity Employer/Sex/Race/Color/Veterans/Disability/Sexual Orientation/Gender Identity or Expression/Religion/Age

About Us | Our Culture | What It’s Like to Work Here | Perks & Benefits

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Cómo se compara este salario de Data Engineer

Este puesto paga $168,800/yren línea con el rango típico para los puestos de Data Engineer.

$144,750 la mediana de $167,600 $186,430

Rango típico $155,676–$172,775/yr, a partir de 22 ofertas comparables de Data Engineer en JobsRadar (salario anualizado en USD). Ver datos salariales de Data Engineer →

Sobre The Hartford

Every day, a day to do right. Showing up for people isn’t just what we do. It’s who we are – and have been for more than 200 years. We’re devoted to finding innovative ways to serve our customers, communities and employees—continually asking ourselves what more we can do. Is our policy language as simple and inclusive as it can be? Can we better help businesses navigate our ever-changing world? What else can we do to destigmatize mental health in the workplace? Can we make our communities more equitable? That we can rise to the challenge of these questions is due in no small part to our company values that our employees have shaped and defined. And while how we contribute looks different for

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