Data Engineer at Hugo Technologies

As entrepreneurs ourselves, we understand the unique challenges startups face managing their rollercoaster growth. We’ve lived it. We know that even well-funded teams can lack the bandwidth to recruit, train, and integrate the operations staff needed to meet growing demand. And that even when the right employees are in place, many companies lack the crucial mid-management layer needed to drive employee performance and process improvements. Hugo was created with the high-growth startup in mind. We custom build or augment existing operations teams for companies in scaling mode, leaving founders and senior management to focus on what matters most: growth.

Key Responsibilities

• Data Infrastructure & Ingestion: Architect and maintain robust, scalable data pipelines that automate ingestion from both structured and unstructured sources. Leverage Python and SQL to ensure reliable, secure, and traceable data flow across platforms.
• Transformation Layer Management: Implement and manage transformation workflows using dbt, ensuring data models are modular, version-controlled (via Git), and optimized for maintainability and performance.
• Pipeline Orchestration: Orchestrate and schedule data workflows using tools such as Airflow or Prefect, ensuring reliable, timely, and automated data delivery across systems.
• Cloud Warehouse Architecture: Design and oversee the data warehouse environment in platforms such as BigQuery or Snowflake, with a focus on security, cost optimization, and high performance.
• Performance Optimization: Continuously monitor query performance and data pipeline throughput, identifying and resolving bottlenecks or inefficiencies in real-time.
• Quality & Governance: Enforce best practices for data validation, lineage tracking, testing frameworks, and schema evolution to maintain high levels of data integrity and trust.
• CI/CD for Data: Set up version control workflows for data transformations and metadata changes, integrating GitOps and CI/CD principles into the analytics engineering lifecycle.
• Cross-functional Partnership: Collaborate deeply with analysts, business users, and product teams to understand evolving data needs and proactively adjust architecture and pipelines.
• Strategic Foundation for Growth: Lay the groundwork for a modern data platform that is scalable, cost-efficient, and primed for future team expansion. This role is expected to evolve into a leadership position as the team grows.

What Qualifications You’ll Need

• Deep expertise in SQL and Python for data engineering and analytics tasks.
• Advanced experience with dbt for modular data modeling and transformation.
• Familiarity with Git, Git-based workflows, and CI/CD pipelines for analytics codebases.
• Proven hands-on experience with cloud data warehouses (BigQuery, Snowflake strongly preferred).
• Hands-on experience with orchestration tools such as Airflow or Prefect for managing complex data workflows.
• Strong understanding of data warehousing concepts, star/snowflake schemas, and performance tuning.
• Knowledge of data security, access control, and cost monitoring within a cloud DWH environment.
• Strong Project Management Skills: Demonstrated ability to lead complex projects, manage timelines, and deliver results.
• People Leader: Proven ability to lead, mentor, and develop associates, fostering knowledge transfer and skill growth within the team.
• Communication: Excellent verbal and written communication skills, with the ability to articulate ideas and influence stakeholders across diverse functions.
• Drive to Action: Demonstrated ability to translate fuzzy problems into structured outputs, using data and qualitative inputs to drive decisions.
• Working Style: Strong ownership mindset, proactive and self-directed; operates well in environments that require initiative and continuous iteration.

Experiences

• 5 to 7 years of progressive experience in Data Engineering.
• Prior experience in a scale-up, product-led, or data-centric organization.
• Familiarity with BI tools is a plus (Tableau, PowerBI, Looker), though not a core requirement.
• A proven track record of implementing end-to-end data infrastructure projects that are still in production.
• Proven record of high performance and substantial achievements in your past positions.
• Demonstrated leadership ability in a team environment.
• Initiative taker, eager to break new grounds, create opportunities for others.
• Willingness to take personal risks - as seen through leadership roles - in work environment and extracurricular activities.
• Ability to work effectively with people at all levels in an organization.
• Excellent written and oral communication skills, with the ability to present to various audiences and distill key messages in order to effectively inform and persuade.
• Skills to communicate complex ideas effectively

Method of Application

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