WHO YOU'LL WORK WITH
You will sit on the Converse Enterprise Data & Analytics technology team, reporting to the Enterprise Data & Analytics Director based at Converse HQ. You will provide people leadership for data engineers and scrum leads who are also based at ITC. Furthermore, you will be part of an agile scrum team, working closely with data product managers, lead engineers, and other cross-functional team members to deliver data and analytics capabilities for Converse.
WHO WE ARE LOOKING FOR
The Data Engineering Senior Supervisor possesses a unique blend of technical expertise, leadership acumen, and strategic vision. They oversee the development and maintenance of large-scale data systems, ensuring data quality, integrity, and security. From a leadership perspective, they have experience in managing and mentoring teams of data engineers, providing guidance on technical best practices, and fostering a culture of innovation and collaboration. They are skilled in agile project management methodologies and have a proven track record of delivering complex data engineering projects on time and within budget.
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The Data Engineering Senior Supervisor needs to have a deep understanding of the organization's data strategy and is able to align their team's efforts with business objectives. Their expertise enables them to communicate effectively with stakeholders, support technical decision-making, and drive business growth through data-driven insights.
- Experienced in building cloud-scalable, real-time, and high-performance data lake solutions using Databricks, Snowflake, and/or AWS, with proficiency in relational SQL, scripting languages (Shell, Python), and source control tools (GitHub).
- Skilled in developing complex data solutions, with a strong understanding of end-to-end design, data structures, and algorithms. Proficient in workflow scheduling tools (Matillion, Brickflow) and familiar with Terraform, Serverless, and Jenkins.
- Proven ability to lead and influence others, with experience in agile team leadership and influencing stakeholders. Effective communicator, both verbally and in writing, with the ability to identify and resolve people and process-related issues.
- Strong problem-solving and analytical mindset, with the ability to take a broad perspective to identify innovative solutions. Willing to learn new skills and technologies, and able to quickly pick up new programming languages, technologies, and frameworks.
- Ability to understand business needs and develop technical solutions to meet them. Strong understanding of solution and technical design.
- Minimum 6 years relevant work experience; Bachelor's degree or equivalent combination of education, experience or training
WHAT YOU'LL WORK ON
As a Data Engineering Senior Supervisor on the Enterprise Data & Analytics team, you will drive work to completion through leading others along with hands-on development responsibilities. You will deliver quality components of a larger data pipeline to support data analytics products with guidance from experienced peers while working within an agile framework that focuses on delivering quality incremental value. As a Senior Supervisor, you will also be responsible for providing people leadership to data engineers, automation engineers, and scrum leads.
- Be responsible for the team's delivery of scalable data solutions to solve a customer need
- Guide the team in troubleshooting data issues and performing root cause analysis to proactively resolve product issues to improve data quality
- Manage, mentor, and inspire a team; managing performance, goals and development potential
- Build reusable components, frameworks and libraries at scale to support analytics products
- Clean, prepare and optimize data for ingestion and consumption by implementing automated workflows
- Collaborate on the implementation of new data management projects and re-structure of the current data architecture
- Build continuous integration, test-driven development and production deployment frameworks
- Collaboratively review design, code, test plans and dataset implementation performed by other data engineers in support of maintaining data engineering standards