Zinnia
Life and Annuities
SeniorDataEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior Data Engineer at Zinnia. Skills: Data Engineering, Python, SQL, Data Pipelines. Oversee technological choices. Implement data pipelines”
What You'll Achieve.
deliver value; uncover opportunities; make decisions with data; develop deeper predictors; develop insights; drive business strategy; build solutions; optimize experiences; deliver modern data pipeline components; deliver efficient data pipeline components; data maximization
Industry & Context.
solve for; novel solutions; complex problems
What They're Looking For.
Must Have
Data Engineering, Data Pipelining, Warehousing, ETL Tools, Python, SQL, Airflow, dbt, Jira, git, buildkite, Terraform, Snowflake, containers, GCP suite, kubernetes, cloud functions, ETL patterns, data warehousing ideas, data mesh, data vaulting, data quality practices, test driven design, data observability
Nice to Have
GCP Professional Data Engineer, AWS Data Analytics, Databricks Certified, dbt Certified
What You'll Do.
Oversee technological choices
Implement data pipelines
Implement warehousing philosophy
Execute cross-organizational projects
Serve as lead on projects
Automate data value chain processes
Promote technical best practices
Design simple data architecture
Design maintainable data architecture
Enable efficient data work
Mentor data team members
Serve as knowledge source
Design data pipelines
Utilize event driven software
Utilize streaming software
Partner with data scientists
Partner with engineers
Ensure reliability in data pipelines
Enforce data governance
Enforce data security
Enforce data protection
Demonstrate innovation
Demonstrate customer focus
Demonstrate experimentation mindsets
Maximize downstream data
Evaluate new engineering tools
Champion new engineering tools
How You'll Work.
Team & Collaboration
Partner with product and engineering teams; Collaborate with smart, creative professionals
Communication Scope
speak the language of statisticians; speak the language of software engineers
Process & Methodology
cross-organizational projects, cross-divisional projects
Full Job Description
WHO WE ARE: Zinnia is the leading technology platform for accelerating life and annuities growth. With innovative enterprise solutions and data insights, Zinnia simplifies the experience of buying, selling, and administering insurance products. All of which enables more people to protect their financial futures. Our success is driven by a commitment to three core values: be bold, team up, deliver value – and that we do. Zinnia has over $180 billion in assets under administration, serves 100+ carrier clients, 2500 distributors and partners, and over 2 million policyholders. WHO YOU ARE: Our data team serves Zinnia through data engineering, data analysis, and data science. Our goal is to help uncover opportunities and make decisions with data. We partner with all department stakeholders across the company to develop deeper predictors of behavior, develop insights that drive business strategy and build solutions to optimize our internal and external experiences. WHAT YOU’LL DO: Overseeing technological choices and implementation of data pipelines and warehousing philosophy Execute and serve as lead and/or SME on cross-organizational and cross-divisional projects automating our data value chain processes Promoting technical best practices throughout the data organization Design data architecture that is simple and maintainable while enabling Data Analysts, Data Scientists, and stakeholders to efficiently work with data. Mentor data team members in architecture and coding techniques. Serve as a source of knowledge for the Data Engineering team for process improvement, automation and new technologies available to enable best-in-class timeliness and data coverage Design data pipelines utilizing ETL tools, event driven software, and other streaming software. Partner with both data scientists and engineers to bring our amazing concepts to reality. This requires learning to speak the language of statisticians as well as software engineers. Ensure reliability in data pipelines
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