Détails du poste

About McKesson Compile

Established in 1833, McKesson is a US Fortune 10 global leader in healthcare supply chain management solutions, retail pharmacy, healthcare technology, community oncology, and specialty care. We partner with life sciences companies, manufacturers, providers, pharmacies, governments, and other healthcare organizations to help provide the right medicines, medical products, and healthcare services to the right patients at the right time, safely and cost effectively.

Based in Bangalore India, McKesson Compile’s data is a comprehensive, full linked system of record for the US Healthcare market, with intelligence on 2M+ healthcare professionals (HCPs) and over 800K facilities. Compile’s data includes high capture medical and pharmacy claims, closed capture Medicare claims (100%), along with best-in-class provider affiliations and customer master.

At McKesson we deliver careers with purpose and potential. Our focus on better health starts with creating an inclusive environment with strong values where you can build a fulfilling career. You can count on us to provide you with resources and opportunities to grow and be your best, while contributing to our pursuit of improving lives.

Data Operations Engineer

 Responsibilities and Duties

● Managing data Operations - from data ingestion to the final delivery of the Product to our

clients/internal stakeholders. The process includes handling RWD, unstructured

healthcare data, and other associated datasets.

● Needs to coordinate with the vendors and work with the Data Engineering team to load

the data into the warehouse.

● Work with stakeholders including the Product, Data, and Design teams to assist with data[1]related technical issues and support their needs.

● Meet the delivery SLAs to load/deliver the data and make it available for internal and

external stakeholders

● Should assess structural modifications, volume consistency, fill rate consistency, and

comprehensive data

● Investigate production problems to identify the underlying causes and suggest fixes

● Collaborate with cross-functional teams to troubleshoot data problems and provide

solutions.

● Assess the integrity of internal data sources and processes, work with project managers,

client service managers, and other internal teams to address problems, implement

preventative measures, and enhance procedures.

● Identify, design, and implement internal process improvements: automating manual

processes, optimizing data delivery, etc.

● Shall keep themselves and the team updated about market events that will affect the data

quality in the next few refreshes.

 What makes you a great fit:

● 3-5 years of Hands-on experience working with healthcare data types, including RWD

(real-world data), claims, EHR, unstructured patient data, etc

● Hands-on experience in critical problem-solving, writing advanced database queries, and

SQL utilities to perform data extraction, data analysis, and working with the data

engineering team.

● Proficiency in Data modeling, validation, quality check, and data engineering concepts.

● Experience working with big-data processing technologies using - databricks, dbt, S3,

Delta lake, Deequ, Griffin, Snowflake, BigQuery.

● Familiarity with version control technologies, and CI/CD systems.

Data Quality Assurance

 Responsibilities and Duties

● Write and review QA plans.

● Validate all incoming and outgoing data

● Meet the quality SLAs

● Run test cases, identify issues, and work with the team to resolve them

● Identify any customer-impacting issues and communicate them beforehand

 What makes you a great fit:

● 2-3 years of Hands-on experience working with analytical data

● Should be able to run the automated, semi-automated, manual tests

● Experience working with healthcare data types, including RWD (real-world data), claims,

EHR, unstructured patient data, etc is a plus.

● Hands-on experience in critical problem-solving, writing advanced database queries, and

SQL utilities to perform data extraction, data analysis, and working with the data

engineering team.

● Experience working with QA frameworks like Great Expectations