Zinnia - Associate Director - Data Engineering

Job Description

What Youll Do Oversee the technological choices and implementation of data pipelines and warehousing strategies Design data sets that are simple, maintainable, and enable Data Analysts, Data Scientists, and stakeholders to work efficiently with data Lead cross-organizational efforts aimed at automating our data processes and data-as-a-service capabilities Advocate for and implement technical best practices throughout the data organization to ensure high-quality outcomes Mentor data team members fostering a culture of learning and continuous improvement Partner with other leaders to bring innovative concepts to reality and design data models for downstream data maximization Ensure reliability in data pipelines and enforce data governance, security, and protection of customer information while balancing technical debt. Evaluate and champion new engineering tools that enhance efficiency and scalability within the Youll Need : A Bachelor/Masters Degree in Computer Science or related field with 10+ years of experience in Data Engineering. Extensive experience with data engineering techniques, including proficiency in Python and SQL. You are acquainted with designing custom Machine Learning (ML) pipelines that integrate into production environments that are customer facing. You have experience working on product teams but also collaborating with other data science team members. You can communicate with a team and articulate ideas to both team members and non-technical stakeholders. Strong Working knowledge of Airflow, dbt, Jira, Git, and Data Warehousing tools (BigQuery, Snowflake) Experience in tools such as GitHub Actions, Terraform, containers Proven track record in leading data engineering teams, mentoring staff, and promoting a culture of learning and teaching. Demonstrated ability to take risks when seeking novel solutions to complex problems and to persist in the face of You Will Use & Must Be Well-versed With : Python for data pipelining and automation Airbyte and Airflow for ETL purposes. GCP or AWS platforms utilizing tools like Terraform, Container services (GKE/EKS), Cloud Functions / Lambda, BigQuery/Snowflake dbt for data modeling. PowerBI for data visualization and consumer-facing dashboard (ref:hirist.tech) Show more Show less