Data Engineer

SVAM International
United States
16 days ago
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Role details

Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
6 years minimum
Working hours
Shift work
Job source

Tech stack

HTML Java (Programming Language) JavaScript (Programming Language) Microsoft Windows Business Analytics Applications Big Data C Sharp (Programming Language) Unix Information Systems Databases Cron Data Architecture
+32 more
Data Files Extract Transform Load (ETL) Data Transformation Data Structures Data Stores Data Warehousing Relational Databases Desktop Computing Metadata Microsoft Office NoSQL Object-Oriented Software Development Oracle Databases Oracle (Applications) Oracle SQL Developer Systems Development Life Cycle Queueing Systems Cloud Services Standard Sql Shell Script PL-SQL SQL Databases Workflow Management Systems Extensible Markup Language (XML) Scripting .NET Core Sql Optimization Information Technology Data Management Data Delivery Stream Processing Data Pipelines

Job description

The Sr. Data Engineer is responsible for expanding, re-engineering and maintaining Client s Data Warehouse environment including its overall architecture, pipelines, and data from internal and external sources. This role supports the delivery of analytics solutions and involves building and optimizing analytics data ecosystems from the ground up. The Sr. Data Engineer will enhance ETL processes through development, setting standards and oversight. They will lead a team of analytics developers, data analysts, and data scientists on various data initiatives, the engineer will ensure optimal and consistent delivery of data architecture across all projects. This role demands a self-directed individual comfortable with Data Warehousing, ETL, and analytics technologies. The Sr. Data Engineer will contribute to the optimization or redesign of the company s analytics data management ecosystem, supporting current and future products and data initiatives. Often taking the lead on projects and development efforts, they will guide them through all stages of SDLC and review and recommend approval for code generated by other team members., Creates and maintains efficient and effective data pipeline architecture and ETL.

Ability to gain/procure business requirements with all levels of stakeholders, and leverage expert knowledge to assemble large, complex data sets that serve as a foundation for analytics, reporting and insights.

Identify, design, and implement internal improvements: automating manual processes, optimizing data delivery, enhancing infrastructure for greater efficiency, extensibility, scalability, etc.

Build infrastructure for optimal extraction, transformation, and loading of data from a wide variety of clinical, operational and financial data sources.

Design and develop analytics tools to provide actionable insights into member outcomes, operational efficiency and other key performance indicators.

Cultivate strong relationships with stakeholders at all levels of the organization on data-related issues and needs. Key role in workgroups and committees as an expert analytics advisor.

Optimize data management to support descriptive and predictive analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader.

Seen as a strong SME while advising data and analytics experts to unlock the potential of data managed by the Data Management team.

Adheres to department standards, practices and P&Ps.

Other duties as assigned.

Requirements

100% remote Need to work as per PST timings

VERY IMPORTANT MUST HAVE HEALTH PAYER / HEALTH PLAN EXPERIENCE

IDEAL CANDIDATE WILL HAVE AROUND 15 YEARS RELEVANT EXPERIENCE, Knowledge / Skills / Abilities

Advanced SQL knowledge and experience working with relational databases, query authoring (SQL) as well as working familiarity with a variety of databases.

Working knowledge of health plan functions and related data required.

Strong analytic skills related to working with health plan data sources and creating health plan-oriented analytics data products.

Building processes supporting data transformation, quality, data structures, metadata, dependency and workload management.

Working knowledge of message queuing, stream processing, and highly scalable big data data stores.

Superior communication skills, both written and spoken.

Outstanding abilities to create, edit and publish documentation for technical products and business user consumption.

Strong project management and organizational skills.

Proficiency in SQL Server and/or Oracle products including (but not limited to): Oracle SQL*Plus, SQL, PL/SQL, Java, JavaScript, HTML, XML, Linux/UNIX shell scripting; knowledge of Oracle RDBMS database, .Net Core, C#, Internet, and Intranets; experience with personal computers (PCs), MS Windows operating system, and MS Office Suite.

Superior problem solving and troubleshooting capabilities.

Excellent verbal and written communication skills.

Ability to attend meetings and participate in small teams effectively.

Must be able to budget time and meet deadlines.

Education and Experience

6+ years experience in a Data Engineer or similar role.

Health plan experience required.

Bachelors degree in Computer Science, Statistics, Informatics, Information Systems or another quantitative field, or equivalent work experience.

Experience with relational SQL and NoSQL databases.

6+ years working in an Oracle environment and advanced knowledge of PL/SQL.

Must know basic Unix scripting and familiarity with Cron Jobs.

Experience building and optimizing big data data pipelines, architectures and data sets, and ETL workflow management tools.

Experience with cloud service environments.

Experience with object-oriented/object function scripting languages.

Experience working with business users to understand their function, processes and goals and incorporate this knowledge into value-added data products.

Experience performing root cause analysis to identify opportunities for improvement.

A successful history of manipulating, processing and extracting value from large disconnected datasets.

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