WHO YOU’LL WORK WITH

As the Lead Data Engineer at Converse, you will play a pivotal role in shaping our Data & Analytics strategy from a product perspective. You will work closely with cross-functional teams, ensuring our data solutions align with business needs and drive measurable impact. Your expertise in data infrastructure, governance, and analytics will be critical in enabling scalable, reliable, and high-performance data capabilities. This is an exciting opportunity to lead the evolution of Converse’s data ecosystem, ensuring our data-driven culture continues to thrive.

WHO WE ARE LOOKING FOR

We are looking for an experienced Lead Data Engineer who excels at both technical execution and strategic thinking. You will own the Data & Analytics product roadmap, ensuring that our data ecosystem is optimized for business intelligence, analytics, and decision-making. You thrive in a collaborative, fast-paced environment, have a strong business acumen, and can translate complex data concepts into actionable insights. The successful candidate will have:

• Proven experience in a data management or engineering role.

• Experience owning Data & Analytics from a product perspective.

• Expertise in data modeling, data warehouse design, and data analysis.

• Hands-on experience with Databricks and Matillion ETL (preferred).

• Strong proficiency in AWS cloud services.

• Experience with version control and collaboration tools, specifically GitHub.

• Strong stakeholder management and communication skills.

• Demonstrated ability in root cause analysis and troubleshooting.

• Excellent business domain knowledge, enabling data-driven decision-making.

• Degree in Computer Science, Information Systems, or a related field.

• Strong leadership qualities with a passion for mentorship and team development.

WHAT YOU’LL WORK ON

As a Lead Data Engineer, you will be responsible for both strategic data initiatives and technical execution, ensuring our data infrastructure supports business needs effectively. Key Responsibilities include:

Product Ownership:

• Own Data & Analytics from a product perspective, ensuring alignment with business objectives.

• Partner with product managers to translate business needs into scalable data solutions.

• Define and drive the data product roadmap, prioritizing key features and improvements.

Business Domain Knowledge:

• Apply strong business acumen to ensure data solutions are contextually relevant and impactful.

• Stay ahead of industry trends and business challenges to refine data strategy.

• Identify opportunities to leverage data for business growth and efficiency.

Design & Development:

• Develop and maintain robust data models, warehouses, and pipelines.

• Utilize Databricks for advanced big data processing and analytics.

• Implement and optimize ETL processes using Matillion ETL and other relevant tools.

Collaboration & Communication:

• Work closely with business stakeholders to understand data requirements and deliver actionable insights.

• Communicate complex data concepts effectively to both technical and non-technical audiences.

Governance & Best Practices:

• Define and enforce data governance policies and best practices.

• Drive initiatives to enhance data processes, automation, and workflows.

• Ensure efficient version control and collaboration using GitHub.

Mentorship & Leadership:

• Mentor junior team members, fostering a culture of continuous learning and innovation.

• Demonstrate thought leadership, driving data strategy and best practices across the organization.