Data Engineer

As our Data Engineer, you'll be foundational in building scalable data pipelines, data warehousing solutions, and real-time analytics infrastructure that unlock powerful insights for our teams and our customers. You'll own the entire data lifecycle from ingestion and transformation to storage and delivery, ensuring our data systems scale reliably with our ambitious growth trajectory.

Responsibilities:

• Design, build, and optimize the data pipeline lifecycle from analytics and reporting solutions.
• Development, ensuring data warehouses and data lakes; ensuring performance, reliability, and scalability.
• Work closely with product, analytics, and engineering teams to identify data requirements and deliver high-quality datasets.
• Implement real-time and batch data processing solutions to support internal reporting and external customer dashboards.
• Ensure data accuracy, integrity, and consistency through rigorous data quality checks and monitoring.
• Champion best practices around data governance, security, and compliance.
• Collaborate to build analytics solutions that drive actionable insights, powering WizCommerce's growth.

Expectations:

• Lead the development of robust and efficient ETL/ELT processes that support analytics and operational use cases.
• Drive initiatives to enhance data quality, reliability, and scalability, directly influencing strategic decisions across the business.
• Establish real-time data processing frameworks and event-driven architectures that enable immediate, actionable insights for internal teams and customers.
• Own the evaluation and integration of new data technologies and tools, continuously optimizing our data stack to meet evolving business needs.
• Spearhead data governance, security protocols, and compliance standards, ensuring our data practices adhere to industry-leading standards.

Requirements:

• 4+ years of professional experience in data engineering, ideally within a fast-paced SaaS or tech company.
• Hands-on experience building and maintaining large-scale data pipelines (ETL/ELT) using modern technologies such as Airflow, dbt, Apache Spark, Kafka, or similar.
• Strong proficiency in SQL, Python, and cloud infrastructure (GCP, AWS, or Azure).
• Proven track record working with cloud-based data warehousing solutions (BigQuery, Redshift, Snowflake).
• Experience implementing monitoring, alerting, and logging for complex data environments.
• Deep understanding of data modeling concepts and schema design.

Preferred Qualifications:

• Familiarity with real-time analytics and event-driven architectures.
• Experience with BI tools such as Looker, Tableau, Power BI, or similar.
• Exposure to containerization and orchestration technologies like Docker and Kubernetes.
• A proactive "builder's mindset" with exceptional problem-solving and analytical skills.