Mgr Data Engineer - Research Data and Analytics

ETL

Roles & Responsibilities

• Team Leadership & Mentorship:

  Lead, manage, and mentor a high-performing team of data engineers, fostering their development and ensuring adherence to best practices.

• Data Pipeline Development:

  Design, develop, and implement data pipelines,

  ETL

  processes, and data integration solutions. You will take ownership of data pipeline projects from inception to deployment, managing scope, timelines, and risks.

• Data Modeling & Optimization:

  Develop and maintain data models for biopharma scientific data, data dictionaries, and other documentation to ensure data accuracy and consistency. You will also optimize large datasets for query performance.

• Collaboration & Solution Design:

  Collaborate with global cross-functional teams, including research scientists, Data Architects, Business

  SMEs

  , Software Engineers, and Data Scientists, to understand data requirements and design end-to-end solutions that meet fast-paced business needs.

• Security & Compliance:

  Implement data security and privacy measures to protect sensitive data.

• Innovation & Process Improvement:

  Explore new tools and technologies to improve

  ETL

  platform performance. You will identify and resolve data-related challenges and adhere to best practices for coding, testing, and designing reusable components.

Technical Skills

• Proficiency in

  SQL

  and

  Python

  for data engineering, test automation frameworks (

  pytest

  ), and scripting tasks.
• Hands-on experience with

  big data technologies

  and platforms, such as

  Databricks

  and

  Apache Spark

  (

  PySpark, SparkSQL

  ).
• Expertise in workflow orchestration and performance tuning on big data processing.
• Good understanding of

  data modeling, data warehousing

  , and

  data integration

  concepts.
• Knowledge of

  cloud data platforms

  (

  AWS

  preferred).
• Experience using

  RDBMS

  (e.g., Oracle, MySQL, SQL Server, PostgreSQL) is a plus.
• Experience with data visualization tools (e.g., Dash, Plotly, Spotfire) is a plus.
• Experience with diagramming and collaboration tools such as

  Miro

  or

  Lucidchart

  is a plus.
• Understanding of data governance frameworks, tools, and best practices is a plus.

• Databricks Certified Data Engineer Professional

  certification is preferred.

Qualifications

• A Doctorate Degree, or a Master's degree with relevant experience, or a Bachelor's degree with extensive experience, or a Diploma with a substantial background in Computer Science, IT, Computational Chemistry, Computational Biology/Bioinformatics, or a related field.
• Experience in implementing and supporting biopharma scientific research data analytics (software platforms) is preferred.

Soft Skills

• Problem-Solving:

  Excellent critical-thinking and problem-solving skills, with the ability to work with large, complex datasets.

• Communication:

  Good communication and collaboration skills, with demonstrated presentation abilities.

• Leadership:

  Ability to engage with business stakeholders and mentor teams to develop data pipelines and data models.

• Proactiveness:

  A passion for tackling complex challenges in drug discovery with technology and data.