Sr. Data Engineer & Scientist
About The Position
The Senior Data Engineer & Scientist is a highly skilled technical professional responsible for designing, building, and maintaining enterprise-scale data platforms, data pipelines, analytics solutions, and AI-enabled applications that support business operations and decision-making across the organization. Reporting to the Director of AI & Automation, this role combines data engineering, data science, analytics, and AI integration responsibilities to establish a modern data ecosystem that enables trusted data intelligence, advanced analytics, predictive insights, and AI-driven solutions. The ideal candidate is hands-on, delivery-focused, and passionate about transforming data into business value while supporting the adoption of AI and automation capabilities across the enterprise. The role collaborates closely with AI Engineers, Automation Engineers, BI Analysts, business leaders, legal domain experts, and other stakeholders to deliver scalable, secure, and reliable data solutions that improve operational efficiency and drive innovation.
Requirements
- Bachelor's degree in Computer Science, Data Science, Data Analytics, Information Systems, Engineering, or a related field (Master's degree preferred).
- 5+ years of professional experience in Data Engineering, Data Science, Analytics Engineering, Automation or a related technical discipline.
- Proven experience designing and implementing enterprise data platforms, data warehouses, lakehouses, and scalable ETL/ELT solutions.
- Strong hands-on expertise in SQL, Python, Spark, and modern cloud-based data platforms.
- Experience building scalable data pipelines, enterprise data integration solutions, and reporting datasets.
- Working knowledge of machine learning, predictive analytics, statistical modeling, and data science methodologies.
- Experience supporting AI initiatives, including Generative AI, Document Intelligence, NLP, RAG, or related AI technologies.
- Strong understanding of data governance, metadata management, data quality, security, and compliance principles.
- Excellent analytical, problem-solving, and stakeholder communication skills.
- Experience working within Microsoft Azure, AWS, or Google Cloud environments and modern DevOps practices.
Nice To Haves
- Azure Data Factory (ADF), Azure Data Lake Storage (ADLS Gen2), Microsoft Fabric, Azure Databricks, AWS RDS, AWS Redshift, AWS Aurora, Google big query, SQL Server, Python, PySpark, Apache Spark, Power BI, Azure Synapse Analytics, Azure Machine Learning, Azure OpenAI, Document Intelligence, LangChain, Vector Databases, Semantic Models, MLOps / LLMOps Fundamentals, GitHub, CI/CD Pipelines, DevOps, APIs, REST Services, Event-Driven Integrations, Workato, Power Automate, Machine Learning, NLP, Predictive Modeling
Responsibilities
- Design, develop, and maintain enterprise data platforms, including data lakes, warehouses, lakehouses, semantic models, and medallion architectures (Bronze, Silver, and Gold layers).
- Build and optimize scalable ETL/ELT pipelines using technologies such as Azure Databricks, Azure Data Factory, Microsoft Fabric, Snowflake, SQL, Python, Spark, AWS, GCP, and similar cloud-native technologies.
- Integrate data from internal applications, third-party systems, APIs, automation platforms, and external data sources.
- Develop enterprise data models, master data structures, and reusable datasets that support reporting, analytics, automation, and AI initiatives.
- Support data migration, modernization, and consolidation efforts across business applications.
- Implement and maintain data governance, cataloging, lineage, data quality, auditing, observability, and security standards.
- Analyze structured and unstructured datasets to identify trends, patterns, opportunities, and actionable business insights.
- Develop predictive models, statistical analyses, and machine learning solutions that improve operational outcomes and decision-making.
- Create and support data products, dashboards, KPIs, and performance metrics for business and executive stakeholders.
- Support experimentation, model evaluation, performance monitoring, and continuous improvement of analytics and machine learning solutions.
- Support the development of AI-enabled applications, Generative AI solutions, Intelligent Document Processing (IDP), and Retrieval-Augmented Generation (RAG) architectures.
- Prepare, curate, and optimize enterprise datasets for machine learning, generative AI, and agentic AI workloads.
- Develop and maintain data pipelines supporting Large Language Models (LLMs), Document Intelligence, vector databases, and AI platforms.
- Enable enterprise search, knowledge management, and contextual data access capabilities.
- Collaborate with AI Engineers to integrate AI capabilities into business applications and workflows.
- Participate in the full solution lifecycle, including requirements gathering, design, development, testing, deployment, monitoring, and support.
- Ensure compliance with enterprise security, privacy, governance, and regulatory requirements.
- Monitor and optimize platform performance, scalability, reliability, and cost efficiency.
- Collaborate with cross-functional teams to deliver high-quality, business-focused solutions.
- Contribute to technical standards, best practices, documentation, and continuous improvement initiatives.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
