Lead Analytics Engineer: Finance, Investment & Actuarial Data

Symetra

United States

Remote

USD 129,000 - 215,000

Full time

14 days+

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Benefits offered by this job

Flexible full-time or hybrid telecommu

401(k) with immediate vesting and 6%匹配

Paid time away (vacation, sick time,),

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Job summary

Symetra seeks a Lead Analytics Engineer to design, build, and maintain scalable data models and data marts for Finance, Investment Management, and Actuarial teams. You will lead migration of investment data from legacy systems to a modern cloud data platform, ensuring quality and accessibility across the organization.

You will mentor engineers, establish best practices, and collaborate with IT and analytics teams to deliver trusted, business-ready data and new analytical capabilities that drive

Qualifications

Bachelor's degree in computer science, data analytics, information systems, mathematics, finance, actuarial science, or related field.
8–10 years of experience in analytics engineering, data engineering, business intelligence, or related data-focused roles.
Advanced expertise in SQL, Python, data modeling, ETL/ELT development, and cloud data architecture.
Experience with modern cloud data platforms such as Snowflake, Databricks, BigQuery, or Amazon Redshift.
Proficiency with analytics engineering and transformation tools such as dbt, SQLMesh, Coalesce, or Dataform.
Experience with workflow orchestration and pipeline management tools such as Dagster, Airflow, Prefect, or Azure Data Factory.
Strong understanding of investment management, investment accounting, insurance asset data, performance measurement, and actuarial analytics.
Demonstrated success leading complex data initiatives, influencing technical direction, and delivering enterprise-scale analytics solutions.

Responsibilities

Design, build, and maintain scalable data models, curated datasets, and data marts that deliver trusted, business-ready data for Finance, Investment Management, and Actuarial teams.
Serve as the subject matter expert for investment and actuarial data domains, including holdings, transactions, security master data, valuations, performance, and attribution data.
Lead the migration and transformation of investment data from legacy platforms to a modern cloud-based data ecosystem, ensuring data quality, consistency, and accessibility.
Develop and optimize cloud-native data architectures that enable efficient storage, integration, governance, and analysis of critical financial and investment data assets.
Build robust ETL/ELT pipelines that integrate structured and unstructured data from multiple internal and external sources while ensuring scalability, reliability, and performance.
Partner with Finance, Investment Management, Actuarial, IT, and Analytics teams to translate requirements into scalable data solutions.
Identify opportunities to create new data products, analytical capabilities, and reporting solutions that improve business decision-making and support strategic objectives.
Apply advanced SQL and Python skills to design data transformations, automate processes, improve data quality, and optimize analytics workflows.
Establish and promote data engineering best practices, including data modeling standards, testing frameworks, documentation, version control, lineage tracking, and governance processes.
Mentor and support analytics engineers with technical guidance, code reviews, architectural recommendations, and development coaching.
Optimize data warehouse performance, schema design, access controls, and pipeline orchestration across Snowflake, Databricks, Redshift, or BigQuery.
Stay current on emerging technologies, tools, and industry trends in data engineering, cloud platforms, investment analytics, and insurance data management.

Skills

SQL

Python

Data modeling

ETL/ELT

Cloud architecture

Snowflake

Databricks

BigQuery

Redshift

dbt

SQLMesh

Coalesce

Dataform

Dagster

Airflow

Prefect

Azure Data Factory

Investment analytics

Actuarial analytics

Education

Bachelor's degree or equivalent experience

Tools