As the Manager of Analytics Engineering at OnePay, your main responsibilities include developing and maintaining the analytical environment, building data solutions like pipelines, dashboards, and data marts, and setting the vision for enterprise-wide analytics usage. You'll lead a team of analytics engineers and promote a data-driven culture within the organization.

To qualify for the Manager, Analytics Engineering position at OnePay, candidates should possess 5-10 years of relevant experience, with at least 3 years in consumer financial companies preferred. A deep understanding of modern data stacks, as well as a proven ability to work cross-functionally to identify data needs, is essential.

OnePay fosters a data-driven culture by encouraging the Manager of Analytics Engineering to evangelize analytics across the enterprise. This involves setting a vision for analytics use, creating actionable insights, and collaborating with diverse teams to ensure data is utilized effectively for decision-making.

In the Manager, Analytics Engineering role at OnePay, familiarity with modern data stack tools like Databricks, DBT, and Monte Carlo is preferred. Experience in managing growing analytical environments and building innovative data solutions will also be integral to your success.

As the Manager of Analytics Engineering at OnePay, you will lead a team of high-performing analytics engineers. Your role will involve mentoring them, inspiring a collaborative work environment, and ensuring that they have the resources and support needed to excel in developing analytics solutions.

In a financial services company like OnePay, I would start by assessing the current data landscape to understand existing processes and data flows. Then, I would collaborate with key stakeholders to define business goals and requirements, ensure data quality, and deploy suitable analytics tools and methodologies to build a robust analytical environment.

I once led a project where my team developed an analytics dashboard for tracking customer financial behaviors. By collaborating closely with cross-functional teams, we identified key metrics, built the dashboard on a modern data stack, and trained stakeholders to interpret the insights, resulting in improved decision-making and enhanced customer engagement.

Vital metrics for evaluating an analytics team's performance could include project delivery timelines, the impact of insights on business decisions, stakeholder satisfaction scores, data quality rates, and team members' professional growth. These metrics provide a comprehensive view of both the effectiveness and efficiency of the team.

To ensure data quality and integrity, I implement a set of best practices including thorough data validation processes, regular audits, and leveraging automated testing tools. I also promote a culture of accountability within the team where everyone is responsible for maintaining high data standards.

In my previous roles, I have extensively worked with modern data stack technologies such as Databricks for data engineering and analysis, DBT for transforming data, and various visualization tools for dashboarding. This experience has enabled me to efficiently construct data pipelines and provide actionable insights.

To promote analytics adoption, I would first identify champions in various business units who are keen on data-driven decision-making. I’d provide them with training and tools, showcase quick wins through analytics, and create a shared analytics platform for ease of access to insights, fostering a collaborative approach to analytics.

When managing competing priorities, I prioritize clear communication and transparency with stakeholders. Regularly syncing via status updates helps manage expectations while setting clear criteria for prioritization based on business impact and urgency ensures we focus on the highest value projects.

I would take a proactive approach to mentoring by conducting regular one-on-one meetings, establishing professional development plans tailored to each team member’s goals, facilitating cross-training opportunities, and fostering a culture of knowledge sharing through weekly brainstorming sessions.

I stay updated with the latest trends in analytics and data engineering by regularly attending industry conferences, participating in webinars, and following thought leaders in the field through blogs and podcasts. This continuous learning approach helps me bring innovative ideas to my role.

Stakeholder collaboration is crucial to my approach as it ensures that the analytics solutions developed meet the true business needs. I prioritize building relationships with stakeholders to understand their challenges and involve them in the analytics process, resulting in better-targeted insights and stronger organizational alignment.