Manager, Data Engineer

Role Overview:

A Data Engineer is responsible for designing, building, and maintaining robust data pipelines and infrastructure that facilitate the collection, storage, and processing of large datasets. They collaborate with data scientists and analysts to ensure data is accessible, reliable, and optimized for analysis. Key tasks include data integration, ETL (Extract, Transform, Load) processes, and managing databases and cloud-based systems. Data engineers play a crucial role in enabling data-driven decision-making and ensuring data quality across organizations.

What you will do in this role:
Design, develop, and maintain data pipelines to extract data from various sources and populate a data lake and data warehouse. Work closely with data scientists, analysts, and business teams to understand data requirements and deliver solutions aligned with business goals. Build and maintain platforms that support data ingestion, transformation, and orchestration across various data sources, both internal and external. Use data orchestration, logging, and monitoring tools to build resilient pipelines. Automate data flows and pipeline monitoring to ensure scalability, performance, and resilience of the platform. Monitor, troubleshoot, and resolve issues related to the data integration platform, ensuring uptime and reliability. Maintain thorough documentation for integration processes, configurations, and code to ensure easy onboarding for new team members and future scalability. Develop pipelines to ingest data into cloud data warehouses. Establish, modify and maintain data structures and associated components. Create and deliver standard reports in accordance with stakeholder needs and conforming to agreed standards. Work within a matrix organizational structure, reporting to both the functional manager and the project manager. Participate in project planning, execution, and delivery, ensuring alignment with both functional and project goals.
What should you have
Bachelors degree in Information Technology, Computer Science or any Technology stream. 3+ years of developing data pipelines data infrastructure, ideally within a drug development or life sciences context. Demonstrated expertise in delivering large-scale information management technology solutions encompassing data integration and self-service analytics enablement. Experienced in software/data engineering practices (including versioning, release management, deployment of datasets, agile related software tools). Ability to design, build and unit test applications on Pyspark for both batch and streaming requirements, which will require in-depth knowledge on Databricks. Experience working with storage frameworks like Delta Lake/ Iceberg Experience working with MPP Datawarehouse s like Redshift Cloud-native, ideally AWS certified. Strong working knowledge of at least one Reporting/Insight generation technology Good interpersonal and communication skills (verbal and written). Proven record of delivering high-quality results. Product and customer-centric approach. Innovative thinking, experimental mindset. Mandatory Skills
Foundational Data Concepts SQL (Intermediate / Advanced) Python (Intermediate) Cloud Fundamentals (AWS Focus) AWS Console, IAM roles, regions, concept of cloud computing AWS S3 Data Processing Transformation Apache Spark (Concepts Usage) Databricks (Platform Usage), Unity Catalog, Delta Lake Data Storage Querying Amazon Redshift / Azure Synapse Trino / Equivalent AWS Athena / Query Federation Data Quality Governance Data Quality Concepts / Implementation Data Observability Concepts Collibra / equivalent tool DevOps Automation CI / CD concepts, Pipelines (GitHub Actions / Jenkins / Azure DevOps)