Essential Skills/Experience:
- - A Masters Degree in Computer Science, Information Systems, Engineering, Business, or related scientific/technical field preferred.
- - Minimum of 10 years of experience in data engineering, business analysis, and data management.
- - Exceptional verbal and written communication skills; ability to convey analytical insights in actionable business terms.
- - Highly motivated self-starter with confidence to present complex information effectively to all audiences.
- - Strong analytical, logical thinking, and organizational skills; capable of managing multiple projects simultaneously.
- - Ability to anticipate future business trends and integrate them into IT and business practices.
- - Proven track record of effective functional and multi-functional collaboration and leadership.
- - Diligent self-starter; able to work independently and in a team environment.
- - Desire and ability to learn/implement new tools and analytic capabilities.
- - Experience designing methods, processes, and systems for consolidating and analyzing structured/unstructured data from diverse sources.
- - Experience developing advanced software applications, algorithms, querying, and automated processes for data evaluation.
- - Proven ability to design complex, large-scale data solutions that are scalable, robust, secure, and resilient.
- - Pharmaceutical or Life Sciences industry experience a plus.
- - Experience using dbT, Fivetran, GitHub, Apache Airflow.
- - Extensive hands-on experience with SQL, Python, ETL/ELT frameworks, and data orchestration pipelines.
- - AWS Architecture Framework knowledge and certification.
- - Expertise in Snowflake concepts like resource monitors, RBAC controls, virtual warehouse sizing, query performance tuning, Zero copy clone, data sharing, time travel, SnowSQL, SnowPipe, Streamlit, Cortex.
- - Experience in data quality and observability tools/methodologies.
- - Understanding of FAIR and TRUSTed data product principles.
- - Knowledge of data governance frameworks/compliance standards relevant to life sciences industry (GDPR/HIPAA).
- - Experience with ETL/ELT/Data Loading tools using Apache Airflow, AWS Glue with Python.
- - Experience bringing to bear AI technologies for ELT processes and automating self-healing data pipelines.
- - Experience working with data science operations teams using serverless architectures, Kubernetes, Docker/containerization.
- - Solid understanding of analytic data architecture/data modeling concepts/principles (data lakes/warehouses/marts).
- - Data warehousing methodologies/modeling techniques (Kimball/3NF/Star Schema).
Desirable Skills/Experience:
- - Prior experience of 10+ years as a Data Platform or Technical Leader in biotech/pharma industry.
- - Advanced experience with cloud platforms beyond AWS (Azure/Google Cloud/Databricks) for data engineering/storage solutions.
