Director of Data Engineering & Analytics
Role summary
FinThrive is seeking a Director of Data Engineering & Analytics to lead the evolution of its enterprise data platform. This senior technical and people leader will own architecture, scalability, governance, and delivery, while enabling analytics for business decision-making. Responsibilities include defining data strategy, leading data engineering and analytics teams (hiring, coaching, performance management), driving data initiatives, and acting as a data architecture evangelist. The ideal candidate possesses a Bachelor's degree in a related field, deep hands-on experience architecting enterprise-scale data platforms, strong cloud expertise (Azure preferred), fluency in SQL and Python, and excellent communication skills.
About the Role
Impact you will make
As
Director of Data Engineering & Analytics
, you will own the evolution of FinThrive’s enterprise data platform—driving architecture, scalability, governance, and delivery. This role serves as the senior technical and people leader for data engineering, while enabling analytics capabilities that power business and product decision-making.
You will partner closely with product, engineering, and business leaders to establish a cohesive data strategy, ensure execution excellence, and develop high-performing teams that deliver measurable value across the organization.
What you will do
Own the architecture and evolution of FinThrive’s enterprise data platform, including ingestion, processing, storage, governance, and consumption.
Define and promote data architecture standards, best practices, and common frameworks across teams.
Lead and develop data engineering and analytics teams, including hiring, coaching, performance management, and delivery accountability.
Drive execution of data initiatives to meet product commitments, timelines, and budget targets.
Partner with product, engineering, analytics, and business leaders to translate requirements into scalable data solutions.
Act as the primary data architecture and strategy evangelist—reviewing designs, aligning stakeholders, and guiding technical decision-making.
Provide architectural guidance and escalation support for client-facing data solutions and implementations as needed.
Present data strategy, architecture decisions, and recommendations to senior leadership in clear, accessible terms.
Continuously improve the data platform through technology modernization, cost optimization, and compliance alignment.
Evaluate and introduce modern data and AI-driven development tools to improve productivity, quality, and delivery velocity
What you will bring
Bachelor’s degree in Computer Science, Data Engineering, Applied Science, or a related field.
Deep, hands-on experience architecting enterprise-scale data platforms, including data modeling, integration, and solution design.
Proven experience leading enterprise data initiatives involving data platforms, data strategy, and large-scale data management.
Strong expertise with cloud platforms (Azure preferred; AWS or GCP also valued).
Experience with modern data technologies and services such as Databricks, Azure SQL, Azure Data Factory, ADLS, Event Hub, Snowflake, or equivalent
Fluency in SQL, data visualization, and at least one programming language (e.g., Python, Java, R, C#)
Demonstrated experience designing and modernizing both greenfield and brownfield environments, across onprem and cloud.
Experience building, governing, and scaling data warehouses, data lakes, or lakehouse architectures.
Strong understanding of Agile (Scrum) methodologies and DevOps principles.
Excellent communication, presentation, and stakeholder engagement skills, with the ability to translate complex technical concepts for varied audiences.
What we would like to see
Experience architecting Data Mesh, graph-based data platforms, or domain-oriented data architectures.
Expertise in infrastructure as code and automating production data and ML pipelines.
Experience modernizing or migrating legacy Hadoop-based environments.
Proven collaboration with data science and advanced analytics teams.
Exposure to healthcare data or regulated data environments.
