Option 1: Create a New Profile
- Job Title
- Data Engineer
- Job ID
- Columbia, MD
- Other Location
Dynamic, Entrepreneurial Consulting Company seeking Data Engineer! If you’ve got entrepreneurial spirit and passion, are driven by results, and want to be a part of significant growth, we’re looking for you!
C2G Partners (C2G) is recognized as an award-winning consulting firm and has provided services to some of the world’s best known and most respected organizations. While C2G has worked primarily with clients that are Fortune 500 and mid-sized companies, we also extend services to smaller businesses and non-profits.
C2G is a marketing and analytic consulting company that places consultants in highly strategic marketing and analytic roles, and one of the fastest growing Inc 5000 companies.
If you are ready to embrace the challenge and would like to join our team as one of our Data Scientists, please keep reading!
We work with business leaders to solve clients’ business challenges and improve clients’ business results using advanced analytics techniques. We contribute our Advanced Data Science subject matter expertise to the recommendations and solutions delivered to our clients.
We spend most of our time on getting data into proper shape, performing statistical analyses, developing predictive models and machine learning algorithms to solve clients’ business problems. We evaluate different sources of data, discover patterns hidden within raw data, create insightful variables, and develop competing models with different machine learning algorithms. We validate and cross validate our recommendations to make sure our recommendations will perform well over time.
C2G Partners is actively searching for a Data Engineer. This role will partner with client technical resources as well as C2G team members, providing guidance and solutions for data architectures, data conversions, ETL and implementation of models in a production environment. The ideal candidate has retail experience and can provide technical expertise working with cloud based platforms as well as traditional data warehouse environments.
§ Work with practice leaders and clients to understand how to make data accessible and usable throughout the organization.
§ Defines data environment design for the reporting and modeling/machine learning use cases that is consistent, maintainable and flexible.
§ Works with client and C2G teams to identify use cases and functional requirements that drive the reporting and modeling data solutions.
§ Designs the database structure including tables, views, synonyms, sequences, triggers, procedures, functions, indexes and materialized views as relevant.
§ Provides the framework for integrating source systems with the reporting and modeling data environments – develops the ERD and data dictionaries
§ Implements business rules via stored procedures, middleware, or other technologies.
§ Develops strategies for flexibility and scalability, and defines the future technical architecture direction for the business intelligence reporting and analytical environments.
§ Problem solve with practice leaders to understand how to build the data pipelines that can support the business, formulate different approaches, outline pros and cons for each approach.
§ Work with practice leaders to get client feedback, get alignment on approaches, deliverables, and overall timeline
§ Document data flow, infrastructure and processes.
§ Turn models and machine learning algorithms into implementable production code
o MS or PhD degree in Operation Research, Advanced Analytics, Computer Sciences, Engineering
o 2-10 years’ professional experience in Data Engineering practices, such as:
- data warehousing, optimization, and productionalization with examples of increased responsibility and evolving technologies.
- developing code and/or applications using software such as Pyspark, Python, SQL, Scala, Java, etc.
- deploying machine learning and data science pipelines into production using model management solutions and leveraging CICD solutions (e.g., Jenkins) for automation
- configuring cloud platforms and configuring elastic compute environments in a cloud platform
- Familiarity with and understanding of modern machine learning approaches, algorithms, libraries, and processes for feature selection / engineering
- Experience building containerized applications and deploying those applications using solutions like Kubernetes
- Experience with structured or un-structured data processing tools (SQL, Hadoop, Spark, NoSQL, MySQL, MariaDB, Hive, Pig, etc)
- Comfortable with cloud-based platforms (AWS, Azure, Databricks, Google)
**This role will require up to 25% travel.