• Design and development of data processing, data cleansing, data transformation and QA scripts using Python and SQL • Operations, maintenance and enhancements of existing mission-critical data pipelines and ETLs • Monitoring and regular maintenance of data pipelines, ensuring data reliability and quality standards • Enhancements to meet evolving business requirements • Optimization of ETLs for performance • Migration of legacy data pipelines into more a scalable architecture • Typical data engineering responsibilities including SQL database design, maintenance and optimization • Fetching datasets from external data sources: REST API, JSON or CSV • Performing data migrations and batch data updates • Exploratory projects including Analysis of various datasets to support business requirements • Developing Proof-of-concept of new data pipelines • Working with other data team members to complete common deliverables and provide technical expertise

• Design, build, and optimize Databricks pipelines in an Azure environment • Develop reliable ETL/ELT workflows using PySpark / Spark SQL • Implement and maintain data ingestion from multiple sources (APIs, DBs, cloud storage, etc.) • Improve performance, scalability, and cost-efficiency of Databricks workloads • Work with stakeholders to translate business needs into technical solutions • Ensure best practices around data quality, monitoring, documentation, and maintainability • Collaborate with cross-functional teams (data engineering, analytics, product, etc.)

• Ability to design data pipelines with scalability, high performance and resilience in-built • Ability to code complex requirements as part of large complex data platforms - develop, refine, and implement high quality delivery executions • Deliver project deployments on public clouds and highly secure environments • Integrate with data governance tools, ecosystem products across the cloud landscape • Ability to understand the business requirements and translate into high-level and low-level designs to successfully meets the business objectives. • Collaborate with stakeholders, presenting findings to a non-technical audience • Bring in best practices for coding and building modular data engineering code that can be reused • Stay current with technical and industry developments and standards to ensure effective and advanced applications of data analysis techniques and methodologies. Including AI integrations.

• Lead end-to-end Data Science projects using agile methodologies with a focus on delivering business value. • Perform exploratory analyses to identify patterns, trends and generate actionable insights to support decision-making. • Develop, evaluate and implement statistical, predictive and prescriptive models (supervised and unsupervised). • Act in a consultative manner to understand client pain points and needs throughout the project. • Present results, analyses and recommendations to technical and non-technical stakeholders with clarity and storytelling. • Collaborate actively with the team, sharing knowledge, best practices and new technical approaches. • Support internal requests and strategic initiatives when needed.