Director- Data Engineering

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.