Lead Data Engineer

American Family Insurance•Boston, MA

11d•$131,000 - $220,000•Onsite

About The Position

This position collects, stores, processes and builds applications within our big data platform. Responsible for integrating these applications with the architecture used across the organization.

Requirements

Demonstrated experience providing customer-driven solutions, support or service.
In-depth knowledge of SQL or NoSQL and experience using a variety of data stores (e.g. RDBMS, analytic database, scalable document stores)
Extensive hands-on Python programming experience, with an emphasis towards building ETL workflows and data-driven solutions.
Able to employ design patterns and generalize code to address common use cases.
Capable of authoring robust, high quality, reusable code and contributing to the division’s inventory of libraries.
Expertise in big data batch computing tools (e.g. Hadoop or Spark), with demonstrated experience developing distributed data processing solutions.
Applied knowledge of cloud computing (AWS, GCP, Azure).
Knowledge of open source machine learning toolkits, such as sklearn, SparkML, or H2O.
Solid data understanding and business acumen in the data rich industries like insurance or financial
Applied knowledge of data modeling principles (e.g. dimensional modeling and star schemas).
Strong understanding of database internals, such as indexes, binary logging, and transactions.
Experience using tools for infrastructure-as-code (e.g. Docker, CloudFormation, Terraform, etc.)
Experience with software engineering tools and workflows (i.e. Jenkins, CI/CD, git).
Practical experience authoring and consuming web services.

Responsibilities

Leads an engineering team to meet project deadlines and priorities.
Supervises assigned data engineering team members & activities.
Ensures the quality, completeness, security, privacy, and integrity of data throughout the data lifecycle.
Documents critical workflows and operational support aspects of team’s responsibilities
Develops deep understanding of data sources, granularity, availability, and limitations.
Provides proactive technical oversight and advice to application architecture and development teams fostering re-use, design for scale, stability, and operational efficiency of data/analytical solutions.
Creates maintainable, scalable code to load and manipulate data in the data warehouse.
Facilitates communication upward and across project teams and business stakeholders.