Sr. Data Engineer

Job Description

• Designs and automates deployment of our distributed system for ingesting and transforming data from various types of sources (relational, event-based, unstructured).
• Own end-to-end delivery of AI and ML solutions from problem definition to production deployment.
• Build and maintain data pipelines using PySpark and Spark in Azure Databricks.
• Designs and implements a framework to continuously monitor and troubleshoot data quality and data integrity issues.
• Implements data governance processes and methods for managing metadata, access, retention of data for internal and external users.
• Designs and provides guidance on building reliable, efficient, scalable, and high‑quality data pipelines with monitoring and alert mechanisms that combine a variety of sources using ETL/ELT tools or scripting languages.
• Performs feature engineering and data preparation for machine learning models.
• Deploys ML models and AI agents for real business use cases.
• Designs and implements agentic AI workflows including multi‑step reasoning and tool usage.
• Tracks experiments, manages models, and supports deployment using MLflow.
• Defines and executes model evaluation frameworks including both ML and AI agent performance.
• Designs and implements physical data models to define the database structure and optimizes database performance through efficient indexing and table relationships.
• Participates in optimizing, testing, and troubleshooting data pipelines.
• Designs, develops, and operates large‑scale data storage and processing solutions using distributed and cloud‑based platforms for storing data (e.g., Azure, AWS, Spark, PySpark, Scala, advanced SQL, Hadoop, etc.).
• Uses innovative and modern tools, techniques, and architectures to partially or completely automate the most common, repeatable, and tedious data preparation and integration tasks to minimize manual and error‑prone processes and improve productivity. Assists with renovating the data management infrastructure to drive automation in data integration and management.
• Ensures the timeliness and success of critical analytics initiatives using agile development technologies such as Dev Ops, Scrum, and Kanban.
• Coaches and develops less experienced team members.
• Works independently on ambiguous business problems and converts them into scalable solutions.
• Collaborates with other data engineers, analysts, solution architects, and business teams to deliver solutions.
• Guides and supports team members on data engineering, ML, and AI best practices.
• Writes clean, production‑ready, and well‑documented code.

• System Requirements Engineering – Uses appropriate methods and tools to translate stakeholder needs into verifiable requirements to which designs are developed; establishes acceptance criteria for the system of interest through analysis, allocation and negotiation; tracks the status of requirements throughout the system lifecycle; assesses the impact of changes to system requirements on project scope, schedule, and resources; creates and maintains information linkages to related artifacts.
• Collaborates – Builds partnerships and works collaboratively with others to meet shared objectives.
• Communicates effectively – Develops and delivers multi‑mode communications that convey a clear understanding of the unique needs of different audiences.
• Customer focus – Builds strong customer relationships and delivers customer‑centric solutions.
• Decision quality – Makes good and timely decisions that keep the organization moving forward.
• Data Extraction – Performs data extract‑transform‑load (ETL) activities from a variety of sources and transforms them for consumption by various downstream applications and users using appropriate tools and technologies.
• Programming – Creates, writes, and tests computer code, test scripts, and build scripts using algorithmic analysis and design, industry standards and tools, version control, and build and test automation to meet business, technical, security, governance, and compliance requirements.
• Quality Assurance Metrics – Applies the science of measurement to assess whether a solution meets its intended outcomes using the IT Operating Model (ITOM),…