What are the responsibilities and job description for the Lead Data Engineer position at MathCo?
We are seeking a highly motivated Engineering Consultant with experience in the Pharmaceutical or Life Sciences domain to work on data and analytics-driven initiatives. This role requires a blend of engineering expertise, domain understanding, and consulting capabilities to solve complex business problems for pharma clients. The ideal candidate will work closely with business stakeholders, data scientists, and engineering teams to design and implement scalable solutions, while ensuring alignment with business objectives. The role also demands strong client-facing communication skills and the ability to operate in a fast-paced consulting environment.
Roles & Responsibilities
As a Engineering consultant, you will:
- Collaborate with client stakeholders and cross-functional teams to understand business requirements and translate them into technical solutions.
- Design and support implementation of data engineering and analytics solutions for pharmaceutical use cases.
- Work on data pipelines, data integration, and platform enablement to support analytics and reporting needs.
- Ensure data solutions align with pharma business processes, including commercial, clinical, or patient data workflows.
- Provide consultative recommendations to improve data architecture, analytics capabilities, and decision-making processes.
- Support delivery teams in ensuring data quality, scalability, and performance of solutions.
- Participate in solution design discussions, architecture reviews, and client presentations.
- Collaborate with data scientists and analysts to enable advanced analytics and insights generation.
- Ensure adherence to best practices in data engineering, governance, and compliance within a regulated environment.
Skills Required
- 7 years of experience in understanding of data engineering concepts, including ETL/ELT pipelines, data modeling, and data integration.
- Experience with cloud platforms (AWS, Azure, or GCP) and modern data ecosystems.
- Familiarity with tools such as Python, SQL, Spark, Databricks, Snowflake, or similar technologies.
- Experience working with data orchestration tools (Airflow, ADF, etc.) is a plus.
- Understanding of data governance, data quality, and scalable architecture design.
- Experience in the pharmaceutical or life sciences industry is required.
- Familiarity with pharma data types such as commercial data, patient data, clinical data, or real-world evidence (RWE).
- Understanding of regulatory and compliance requirements in pharma environments is a plus.
- Strong problem-solving and analytical thinking.
- Excellent communication and stakeholder management skills.
- Experience working in client-facing consulting engagements.
- Ability to work in cross-functional and matrixed environments