Data engineering services required:

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Build data products and processes alongside the
core engineering and technology team

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Collaborate with senior data scientists to
curate, wrangle, and prepare data for use in their advanced analytical models

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Integrate data from a variety of sources,
assuring that they adhere to data quality and accessibility standards

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Modify and improve data engineering processes to
handle ever larger, more complex, and more types of data sources and pipelines

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Use Hadoop architecture and HDFS commands to
design and optimize data queries at scale

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Evaluate and experiment with novel data
engineering tools and advises information technology leads and partners about
new capabilities to determine optimal solutions for particular technical
problems or designated use cases /

Big data engineering skills:

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5+ years of hands-on experience in one or more
modern Object-Oriented Programming languages (Java, Scala, Python) including
the ability to code in more than one programming language.

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5+ years of hands-on experience applying
principles, best practices, and trade-offs of schema design to different
database systems, including relational (Oracle, MSSQL, Postgres, MySQL) and
NoSQL (HBase, Cassandra, MongoDB)

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2+ years of hands-on experience implementing
batch and real-time data integration frameworks and/or applications in private or
public cloud environments (AWS, Azure, GCP, etc.) using various technologies
(Hadoop, Spark, Impala, etc.), including assessing performance, debugging, and
fine-tuning those systems

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2+ years of hands-on
experience in developing enterprise level APIs leveraging python web
frameworks, like Flask.

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Deep understanding of the latest data science
and data engineering methods and processes to develop impactful and reusable
patterns and abstractions from enterprise-level data assets

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3+ years of hands-on experience in all phases of
data modeling from conceptualization to database optimization

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Demonstrated ability to perform the engineering
necessary to acquire, ingest, cleanse, integrate, and structure massive volumes
of data from multiple sources and systems into enterprise analytics platforms

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Proven ability to design and optimize queries to
build scalable, modular, efficient data pipelines

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Ability to work across structured,
semi-structured, and unstructured data, extracting information and identifying
linkages across disparate data sets

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Proven experience delivering production-ready
data engineering solutions, including requirements definition, architecture
selection, prototype development, debugging, unit-testing, deployment, support,
and maintenance

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Ability to operate with a variety of data
engineering tools and technologies; vendor agnostic candidates preferred

Domain and industry knowledge:

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Strong collaboration and communication skills to
work within and across technology teams and business units

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Demonstrates the curiosity, interpersonal
abilities, and organizational skills necessary to serve as a consulting
partner, includes the ability to uncover, understand, and assess the needs of
various business stakeholders

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Experience with problem discovery, solution
design, and insight delivery that involves frequent interaction, education,
engagement, and evangelism with senior executives

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Ideal candidate will have extensive experience
with the creation and delivery of advanced analytics solutions for healthcare
payers or insurance companies, including anomaly detection, provider
optimization, studies of sources of fraud, waste, and abuse, and analysis of
clinical and economic outcomes of treatment and wellness programs involving
medical or pharmacy claims data, electronic medical record data, or other
health data

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Experience with healthcare providers, pharma, or
life sciences is a plus