Data Engineer

Contexture•Jenks, OK

8d•Remote

About The Position

The Data Engineer will work as part of the Data Operations & Engineering, Transformation and Analysis team to derive business value from enterprise data by implementing technical specifications provided by the Data Architect and Director of Data Operations & Engineering, including data storage, processing, transformation, ingestion, consumption, and automation. This role will work with multiple healthcare data sources and cross-functional teams to establish integrated datasets across legacy and greenfield data systems/platforms. You will work closely with data producers, consumers, and subject matter experts to enhance data service capabilities by implementing data architecture frameworks, standards, and principals, including modeling, metadata, security, and reference data. This position is based in Phoenix Arizona; Denver Colorado; or Grand Junction, Colorado and requires local residency in one of these base locations. Our strategic flexibility allows for local work from home opportunities.

Requirements

Demonstrated experience with relational and non-relational data storage models, schemas, and structures used in data lakes and warehouses for big data, business intelligence, reporting, visualization, and analytics.
Hands-on experience with extract, transform, load (ETL) process design, data lifecycle management, metadata management, and data visualization/report generation.
Practical experience with industry-accepted standards, best practices, and principles for implementing a well-designed enterprise data architecture.
Data processing experience in a production environment with terabyte-sized datasets that are both structured and unstructured data
Required Languages: Python and SQL
Required Libraries: PyData stack, Dask, and Prefect
Data storage experience with Microsoft SQL Server, MongoDB, and Snowflake
Leveraging data partitioning, parallelization, and data flow optimization principles
Implementing data sterilization, normalization, and standardization processes
Orchestrating fault tolerant/redundant data processing flows
SQL query planning and optimization for incremental and historical workloads
File manipulation across varied file types, encodings, formats, and standards
Secure Software Development Lifecycle (SSDLC), version control and release management
Knowledge of healthcare interoperability standards such as HL7 (Health Level 7), FHIR (Fast Healthcare Interoperability Resources), CDA (Clinical Document Architecture), etc.
Knowledge of healthcare clinical code sets such as LOINC, SNOMED, CPT, ICD-10, etc.
Strong understanding of project management disciplines such as Agile and Waterfall.
Ability to translate technical requirements into actionable tasks for execution.
Ability to work in a fast-paced and rapidly changing environment while consistently meeting strict service level agreement performance requirements.
Ability to work independently as well as ability to effectively work in a team environment and maintain strong working relationships.
Working knowledge of Microsoft Office 365 toolset (OneDrive, Word, Excel, and PowerPoint).
Minimum of 4+ years of experience working in data-related positions with increasing responsibility and scope of duties; specifically, 4+ years working with relational databases, 3+ years working with analytical data workloads, and 2+ years working with batch data processing technologies.
Bachelor’s Degree is required with a concentration in a data-related field such as Computer Science, Informatics, Mathematics, Engineering, etc.

Responsibilities

Develop, implement, and maintain enterprise data management solutions to enable organizational business intelligence, reporting, visualization, and analysis.
Assist with the development, implementation and maintenance of an overall organizational data strategy that is in line with business processes.
Design and build data processing flows to extract data from various sources, such as databases, API endpoints, and flat files.
Load data into data storage systems, specifically Microsoft SQL Server, MongoDB, and Snowflake.
Transform data using industry-standard techniques such as standardization, normalization, de-duplication, filtering, projection, and aggregation.
Build and maintain data processing environments, including hardware and software infrastructure. Working knowledge of data flow orchestration tools such as Prefect and Airflow is preferred.
Collaborate with data producers, consumers, and subject matter experts to ensure smooth dissemination and flow of data within the organization.
Performs other related duties as assigned.