Benefits Summary
• Flexible and hybrid work arrangements
• Paid time off/Paid company holidays
• Medical plan options/prescription drug plan
• Dental plan/vision plan options
• Flexible spending and health savings accounts
• 401(k) retirement savings plan with a Roth savings option and company matching contributions
• Educational assistance program
Overview
The Data Engineer is responsible for designing, building, and optimizing scalable data solutions to support a wide range of business needs. This role requires a strong ability to work both independently and collaboratively in a fast-paced, agile environment. The ideal candidate will engage with cross-functional teams to gather data requirements, propose enhancements to existing data pipelines and structures, and ensure the reliability and efficiency of data processes.
Responsibilities
• Assist with leading the team's transition to the Databricks platform and utilize the newer features of Delta Live Tables, Workflows etc
• Design and develop data pipelines that extract data from Oracle, load it into the data lake, transform it into the desired format, and load it into Databricks data lakehouse
• Optimize data pipelines and data processing workflows for performance, scalability, and efficiency
• Implement data quality checks and validations within data pipelines to ensure the accuracy, consistency, and completeness of data
• Help create and maintain documentation for data mappings, data definitions, architecture and data flow diagrams
• Build proof-of-concepts to determine viability of possible new processes and technologies
• Deploy and manage code in non-prod and prod environments
• Investigate and troubleshoot data related issues and fix or provide solutions to fix defects
• Identify and resolve performance bottlenecks, which could include suggesting ways to optimize and performance tune databases and queries to enhance query performance
Qualifications
• Bachelor's Degree in Computer Science, Data Science, Software Engineering, Information Systems, or related quantitative field
• 4 plus years of experience working as a Data Engineer, ETL Engineer, Data/ETL Architect or similar roles
• Must hold a current/active Databricks Data Engineer/Analyst certification
Skills
• 4+ years of solid continuous experience in Python
• 3+ years working with Databricks with knowledge and expertise of data structures, data storage and change data capture gained from prior production implementations of data pipelines, optimizations, and best practices
• 3+ years of experience in Kimball dimensional modeling (star-schema comprising of facts, type1 and type2 dimensions, aggregates, etc.) with solid understanding of ELT/ETL
• 3+ years of solid experience writing SQL and PL/SQL code
• 2+ years of experience with Airflow
• 3+ years of experience working with relational databases (Oracle preferred)
• 2+ years of experience working with NoSQL databases: MongoDB, Cosmos DB, DocumentDB or similar
• 2+ years of cloud experience (Azure preferred)
• Experience with CI/CD utilizing git/Azure DevOps
• Experience with storage formats including Parquet/Arrow/Avro
• Effectively collaborate with team members while being able to work independently with minimal supervision
• Must have a creative mindset, knack to solve complex problems, passion to work with data, and a positive attitude
• Ability to collaborate within and across teams of different technical knowledge to support delivery and educate end users on data products
• Expert problem-solving skills, including debugging skills, allowing the determination of sources of issues in unfamiliar code or systems
Pluses, but not required: Any work experience in the following:
ETL / ELT tools: Spark, Kafka, Azure Data Factory (ADF)
Languages: R, Java, Scala
Databases: Redis, Elasticsearch
