Staff Data Engineer

Company Description

A Place for Mom is the leading online resource connecting families searching for senior care with a team of expert advisors providing insight-driven, personalized solutions. As the nation’s largest senior care advisory service, A Place for Mom helps hundreds of thousands of families every year navigate the complexities of finding the right senior care solution for their loved ones across home care, independent living, memory care, assisted living, and more. Established in 2000 as a family business, A Place for Mom employees are deeply committed to the company mission to enable caregivers to make the best senior care decisions. A Place for Mom fosters, cultivates, and preserves a culture of diversity, equity, and inclusion.

Our employees live the company values every day:

• Mission Over Me: We find purpose in helping caregivers and their senior loved ones while approaching our work with empathy.
• Do Hard Things: We are energized by solving challenging problems and see it as an opportunity to grow.
• Drive Outcomes as a Team: We each own the outcome but can only achieve it as a team.
• Win The Right Way: We see organizational integrity as the foundation for how we operate.
• Embrace Change: We innovate and constantly evolve.

Job Description

As a Staff Data Engineer, you will play a pivotal role in building and optimizing our data infrastructure to enable robust, data-driven decision-making across the organization. You’ll work hands-on with cutting-edge technologies, lead complex projects, and collaborate with cross-functional teams to deliver scalable data solutions that meet business needs.

Key Responsibilities:

1. Data Engineering Leadership
   • Lead the design and implementation of high-performance data pipelines and workflows using Databricks on AWS.
   • Drive innovation and improvements in data engineering practices and processes across teams.
2. Data Warehouse Development & Optimization
   • Develop, maintain, and optimize our data warehouse architecture for performance, scalability, and reliability.
   • Collaborate with data scientists and analysts to ensure the warehouse supports business intelligence and machine learning needs.
3. ETL Pipeline Development
   • Design and build ETL processes to integrate, transform, and deliver data from multiple sources.
   • Ensure ETL jobs are optimized for efficiency, accuracy, and timeliness.
4. Data Quality & Monitoring
   • Implement tools and frameworks to monitor and maintain data accuracy, consistency, and reliability.
   • Build automated systems for detecting and resolving data quality issues.
5. Collaboration & Mentorship
   • Partner with cross-functional teams, including software engineers, product managers, and business stakeholders, to understand data requirements …