Job Description
The Associate Data Engineer at Arevon is responsible for designing, building, and maintaining scalable data architectures and pipelines to transform large datasets into actionable insights, supporting performance optimization and predictive analytics for renewable energy assets.
Key Responsibilities
- Architect, build, deploy, and maintain scalable data pipelines and architectures for diverse data types.
- Establish and manage a cloud-native Data Lakehouse using platforms like Databricks DeltaLake and Snowflake.
- Process, integrate, and transform large datasets from various sources into central data platforms.
- Develop and deploy analytical and predictive models for anomaly detection, root cause analysis, and failure prediction.
- Manage the complete data lifecycle from raw ingestion to refined data products for analytics and visualization.
- Evaluate and improve software systems, processes, and procedures within the performance and operational engineering teams.
- Create and maintain metadata documentation and enforce data governance and discoverability standards.
Requirements
- Bachelor's or Master's degree in data science, Computer Science, Engineering, Statistics, or a related quantitative field.
- 5 years of professional experience in a Data Engineering, Data Scientist or Machine Learning Engineering role specifically focused on building and deploying production data systems.
- Mandatory hands-on experience building production data platforms primarily on AWS.
- Deep familiarity with core AWS services for data ingestion, storage, orchestration, including S3 primary data lake storage, Lambda serverless functions, SQS SNS event handling and notifications, and CloudWatch monitoring and alerting.
- Expertise in establishing modern Lakehouse layer built on top of S3, utilizing technology like Databricks DeltaLake, Snowflake or Redshift.
- Expertise in Apache Spark PySpark, Spark SQL for processing large, complex datasets with proven ability to tune and optimize spark jobs for cost and performance.
- Expert-level proficiency in SQL and demonstrated ability to design schemas and build pipelines optimized for high frequency time-series analytics.
- Hands-on experience implementing data governance frameworks, security policies or catalogs in a production cloud environment.
- Fluency in Python and common data science libraries like Pandas, NumPy and Scikit-learn.
- Experience developing, training and validating anomaly detection models and time-series forecasting models.
- Experience writing production-grade code that adheres to software engineering standards, including unit testing, integration testing and peer code reviews.
- Expertise in statistical analysis, machine learning, and mathematical modeling.
- Proven track record of working well within a team environment, taking ownership of processes and procedures.
- Understanding of business strategy and the ability to align data insights with organizational goals.
- Excellent interpersonal skills, highly motivated and results oriented, process oriented with strong organizational and communication skills.
Benefits & Perks
Competitive Compensation and Incentives
Generous Paid Time Off
Flexible Work Environment
401 k Plan with 6 Company match
Tuition Reimbursement Program
Paid Parental and Caregiver Leave
Inspiring Company Culture
Professional Development Opportunities
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