Director, Data Engineering

Job Description

Hatch I.T. is partnering with Brado to find a Director, Data Engineering. See details below:

About the Role:
The Director of Data Engineering, owns the data strategy, architecting the right data platform, and serving analytics-ready data products to meet business needs. They lead the development of data pipelines and data products necessary to allow analysts, AI/ML Engineers, and data integrators across Brado’s clients to accomplish their goals. They contribute to the vision for developing our modern data infrastructure on our Microsoft Azure cloud platform. They work closely with fellow engineers, data scientists, and reporting and measurement specialists to establish best practices for creating systems and data products that the business will use. They possess deep technical skills, are comfortable owning the data strategy and data infrastructure, and are excited about building a strong data foundation for the company. The Director of Data Engineering leads a small team of other data engineers and grows junior talent to the best of their capacity.

While this is a remote position, ideal candidates will live near one of our collaboration centers in St. Louis or Dallas/Ft. Worth, so that they can be on-site with teams regularly.

About the Company:
Brado is a tech-enabled marketing services firm. They believe insight and empathetic connection can change not only change a brand, it can change the world.

Benefits:

• Health Care Plan (Medical, Dental & Vision)
• Retirement Plan (401k, IRA)
• Life Insurance (Basic, Voluntary & AD&D)
• Paid Time Off (Vacation, Sick & Public Holidays)
• Family Leave (Maternity, Paternity)
• Short-Term & Long-Term Disability
• Training & Development
• Work From Home

Responsibilities:

• Designs and implements scalable data pipelines and analytics solutions on the Data Bricks platform.
• Designs and implements scalable, high-performance data architectures. Understands data modeling techniques, including relational, dimensional, and NoSQL data models.
• Builds and manages data warehouses using technologies like Amazon Redshift, Google BigQuery, or Snowflake. Optimizes data warehouse performance and cost management.
• Integrates data from core platforms like Marketing Automation, CRM, and Analytics into a centralized warehouse.
• Builds Extract, Load, and Transform (ELT) processes for ingesting and transforming data from various sources into a unified format.
• Proficient in big data technologies such as Hadoop ecosystem (HDFS, Hive, HBase), Apache Kafka, and Apache Flink. Leverages these technologies for large-scale data processing and real-time analytics.
• Programs in languages such as Python, Scala, or Java. Writes efficient, maintainable code for data processing, analytics, and automation tasks.
• Understands data governance principles, data privacy regulations (e.g., HIPAA, GDPR, CCPA), and best practices for ensuring data security and compliance.
• Provides solutions that are forward-thinking in data and analytics.
• Leads and mentors a team of both internal and contracted data engineers.
• Translates technical concepts into non-technical terms and influences decision-making.
• Identifies complex data engineering challenges and devises innovative solutions. Thinks critically and makes data-driven decisions to optimize processes and systems.
• Stay updated with the latest advancements in data engineering, cloud technologies, and industry trends. Adapts to evolving technologies and business requirements.
• Develop and implement quality controls and departmental standards to ensure quality standards, organizational expectations, and regulatory requirements.
• Contribute to the development and education plans on data engineering capabilities, systems, standards, and processes.
• Anticipate future demands of initiatives related to people, technology, budget and business within your department and design/implement solutions to meet these needs.
• Communicate results and business impacts of insight initiatives to stakeholders within and outside of the company.

Requirements

• 10 years of experience with modern data engineering projects and practices: designing, building, and deploying scalable data pipelines with 5+ years of experience deploying cloud-based data infrastructure solutions.
• Strong understanding and hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP). This includes knowledge of cloud services like computing, storage, networking, and databases.
• 3 years of experience building data pipelines for AI/ ML models using PySpark or Python.
• 4+ years of experience building data pipelines with modern tools such as Data Bricks, Fivetran, dbt, etc. including data processing using Apache Spark, Delta Lake, Unity Catalog, and MLflow.
• Familiarity with lakehouse architecture and delta lake.
• BS in Computer Science, Engineering, Statistics, Informatics, Information Systems, or another quantitative field. Master’s degree preferred