Overview

The Data Engineer will build, orchestrate and maintain data pipelines to populate our future single version of truth data warehouse. They will discuss data requirements with other teams in an agile environment and will have the ability to develop proof-of-concepts to evaluate new technologies and suggest improved and better ways to re-architect existing data loading processes and data structures.

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

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Data Engineer