Analytics Engineering Team Lead

Our Mission

At Kaizen, our commitment to the unparalleled value of data drives every decision we make. Building on our robust Lake House foundation, we are transitioning towards a Data Mesh architecture. This approach emphasizes treating data as a product with well-defined contracts, distributed ownership, and domain-oriented design.

As we continue to enhance our data ecosystem, we are focusing keenly on data democratization, governance, and observability. Our goal? To make every piece of data perfectly accessible, monitored, and governed.

Our Data Tribe is well-prepared to lead this transition, championing the design and expansion of next-generation Data Products. By joining our crew, you’ll be part of an initiative that positions Kaizen at the forefront of data-driven innovation.

Let's start with the role

Our Lake House infrastructure has been a foundational pillar, seamlessly supporting a diverse cohort from BI, Data Science and AI teams to our innovative product squads. As we shift towards Data Products and insights driven by a Data Mesh paradigm, we anticipate transformative outcomes. At the heart of this transition, we aim to enhance:

• The robustness of our data pipelines
• The agility of our data products
• The precision of our solutions
• The highest standards of data governance

We are looking for an Analytics Engineering Team Lead to join our Big Data department and lead one of our Analytics teams in delivering our reporting ecosystem. 

As an Analytics Engineering Team Lead, you will drive the design, development, and maintenance of our data flows and modeling to support business analysis and reporting needs, mentor and develop engineers, fostering a culture of collaboration, open communication, and technical excellence. Also, you will improve team performance, boost delivery velocity, and enhance technical expertise.

Our Analytics Engineering teams consist of members with diverse backgrounds and our tech stack includes, but is not limited to:

• Databricks, Delta Lake, dbt Cloud
• SQL Server, Azure Data Warehouse
• SSIS, SSAS, SSRS
• Spark, Apache Airflow, Apache Kafka

As an Analytics Engineering Team Lead, you will:

• Lead your team in adopting best practices for data analysis, system design, integrity, validation, and documentation.
• Work closely with your engineering team and the Product Owner to translate business and functional requirements into scalable, high-performance data solutions.
• Design, implement, and optimize large-scale, high-volume, high-performance data structures to support analytics and data science workloads.
• Build and maintain scalable ETL/ELT transformation pipelines.
• Drive continuous improvement in reporting and analysis by automating and simplifying self-service data modeling and production support.
• Uphold data governance standards, ensuring data products are accessible, monitored, and well-managed.
• Mentor and develop engineers, helping them grow and advance in their careers.
• Partner with management to identify and attract top technical talent.

What you'll bring

• 6+ years of hands-on experience in writing complex, highly optimized SQL code for data manipulation and reporting.
• 4+ years of experience in ETL/ELT, Data Modeling, Data Warehouse Architecture and Reporting tools.
• 2+ years of experience in leading engineering teams.
• Experience in the Microsoft BI stack (SQL Server, SSIS, SSRS, SSAS).
• Proven leadership and mentoring skills, with the ability to guide, support, and elevate your team.
• Strong problem-solving and analytical skills, with the ability to navigate and lead teams through complex technical challenges.
• Ability to meet deadlines with high attention to detail and quality.
• Adaptability and quick learning, with the ability to navigate technical, business, and organizational landscapes.

It’s a plus if you bring:

• Degree in a quantitative/technical field (e.g. Computer Science, Statistics, Engineering) or equivalent industry experience.
• Experience with Azure Data platforms (Synapse Analytics, Data Lake, Databricks). 
• Experience in programming languages, preferably Python or Scala.
• Exposure to CI/CD.
• Knowledge of Data Governance principles and best practices.
• Familiarity with Agile team-working environment.