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Salary: USD 175,000 - 200,000 / annual
Unite Us is seeking a Staff Data Engineer to lead enterprise-wide data architecture strategy and long-term data platform design. This is a strategic technical role responsible for evaluating and approving major technical designs, establishing engineering best practices, and serving as a technical advisor to leadership on product and data roadmap decisions.
Key responsibilities include:
- Lead data architecture strategy and platform design across the enterprise
- Champion data governance, democratization, and scalable data standards
- Design and manage ETL workflows using Airflow, DBT, Python, and Snowflake to support large-scale data ingestion, transformation, and loading
- Build and scale CI/CD pipelines and enterprise-wide data quality frameworks
- Develop streaming pipelines with Confluent Kafka for event-driven processing
- Build dashboards and monitors for pipeline health, freshness, uptime, and SLA compliance
- Apply data modeling techniques to extract and analyze information from large structured and unstructured datasets
- Test internal data pipelines for reliability and performance; validate and reformulate models for accuracy
- Work cross-functionally with Solutions Delivery, Business Intelligence, Predictive Analytics, and Enterprise Services teams to support data-related technical needs
- Analyze and manipulate large datasets using statistical software and programming languages
Requirements:
- Master's degree or foreign equivalent in Data Science, Applied Computer Science, Computer Science, or related field
- Minimum 4 years of experience with ALL of the following: (a) developing and optimizing scalable pipelines using Apache Airflow and Python; (b) building streaming pipelines using Kafka; (c) tuning big data systems using Kafka and Hive; (d) using Spark for batch and streaming analytics jobs; (e) writing and using complex SQL queries for data warehouse analysis and reporting; (f) using AWS for data processing and large-volume workloads; (g) automating deployment of code and processes using Python; (h) working with cloud-based infrastructure; (i) building Datadog dashboards to monitor pipelines and system performance; (j) using Docker and Kubernetes for containerization and automation; (k) implementing CI/CD using Jenkins; (l) using GitHub as code repository, Windows and Linux operating systems. Experience may be gained concurrently.
