Senior Data Engineer, Jobber

• Leadership Skills: Proven ability to lead and collaborate in team environments, fostering a culture of shared success
• Strong coding skills in Python and SQL
• Expertise in building and maintaining ETL pipelines using tools like Airflow and dbt
• Experience working with AWS data infrastructure, particularly Redshift, Glue, Lambda, and ECS Fargate
• Familiarity with handling large datasets using tools like Spark or similar (e.g., Trino)
• Experience with Terraform for infrastructure management
• Experience with dimensional modelling, star schemas, and data warehousing in a cloud environment (preferably AWS Redshift)
• Knowledge of CI/CD processes, data ingestion, and optimizing data flow across systems
• Proficient in working with high-volume, scalable data infrastructure
• Strong Communication: Ability to collaborate effectively with both technical and non-technical teams, explaining complex data concepts in a clear and concise manner

Desirable

• As a Senior Data Engineer, reporting to the Manager of Data Engineering, you’ll be a key contributor on our Data Platform team
• Our team builds Jobber’s data infrastructure and systems, driving improved operational outcomes, enhancing workflow efficiencies, and generating critical business insights. We empower teams across the organization to fully leverage data, tools, and technology to achieve their goals. By researching, developing, and maintaining data systems, we provide essential operational and analytical support to ensure our internal teams are set up for success
• We're seeking individuals who are ready for their next challenge—those who want to use their expertise to influence people, processes, and decisions that have a direct impact on the company's trajectory
• Build Scalable Data Solutions: Design, develop, and maintain batch and real-time data pipelines within cloud infrastructure (preferably AWS). Leverage Python, SQL, and AWS technologies (Glue, Lambda, ECS Fargate) to ensure smooth data operations. Build scripts, serverless applications, and automated workflows
• Empower Internal Teams: Develop tools and frameworks that automate manual processes, set up alerting/monitoring systems, and help teams run data-driven experiments and analyze results. Work closely with cross-functional teams to support their needs and ensure data accessibility
• Accelerate Business Growth: Collaborate with data analysts, scientists, and product teams to extract actionable insights from data. Utilize tools like Airflow and dbt to streamline ETL/ELT pipelines and ensure the seamless flow of data
• Strategic Planning and Innovation: Lead initiatives to research and propose new technologies and tooling for our data stack, with an emphasis on performance and scalability. Participate in design and code reviews, continuously learning from and mentoring your peers
• Data Integrity: Own the integrity of our data and maintain a high level of trust across the organization