Senior Staff - IT Data Engineering and Analytics at StoneX in Pune

Job description

Overview Overview Connecting clients to markets – and talent to opportunity With 4,300 employees and over 400,000 retail and institutional clients from more than 80 offices spread across five continents, we’re a Fortune-100, Nasdaq-listed provider, connecting clients to the global markets – focusing on innovation, human connection, and providing world-class products and services to all types of investors. At StoneX, we offer you the opportunity to be part of an institutional-grade financial services network that connects companies, organizations, and investors to the global markets ecosystem. As a team member, you'll benefit from our unique blend of digital platforms, comprehensive clearing and execution services, personalized high-touch support, and deep industry expertise. Elevate your career with us and make a significant impact in the world of global finance. Business Segment Overview: The Enterprise Data & Technology (EDT) team supports a wide variety of data assets and technologies in support of StoneX business initiatives for institutional and retail customers, while providing data services to almost all areas of the organization. Reporting to the Chief Data Officer, the EDT organization serves multiple internal stakeholders. Corporate: Engage in a deep variety of business-critical activities that keep our company running efficiently. From strategic marketing and financial management to human resources and operational oversight, you’ll have the opportunity to optimize processes and implement game-changing policies. Responsibilities Job Purpose and Responsibilities Position Purpose: The Senior Data Engineer (Forward Deployed) is a hands-on engineer who works alongside the business, not behind a ticket queue. You will embed with trading, operations, finance and risk teams to understand how they work, turn their questions into precise source-to-target mappings, and ship production Databricks pipelines on the StoneX Unified Data Platform in days and weeks, not quarters. AI is a core part of how you work: you use it to analyse sources, draft mappings, generate and test code, and build reusable frameworks that make the whole Global Data Services team faster. The role suits an engineer who enjoys a live problem, a demanding stakeholder and a fast pace, and who can explain a technical trade-off as clearly to a trader as to an engineer. What 'Forward Deployed' means here: Embedded: you spend a large share of your time with business users – in their meetings, their tools and their data – and you own the outcome, not just the code. End to end: discovery → mapping → pipeline → validation → adoption, delivered by one accountable engineer. Fast and iterative: working increments in front of users early, with short feedback loops. Leverage: every engagement leaves something reusable behind (a framework, a template, an agent) so the next one is faster. Technology Ecosystem: Data platform: Databricks (Delta Lake, Unity Catalog, Lakeflow / Workflows, Databricks SQL), Python, PySpark, SQL, SQL Server Integration: RedPanda (Kafka), OpenAPI / REST APIs, Airflow, Profisee MDM, Microsoft Dynamics AI-assisted delivery: Claude Code, GitHub Copilot, Databricks Assistant and AI/BI Genie Engineering: medallion architecture (Bronze / Silver / Gold), data warehousing, CI/CD (GitHub Actions, Jenkins), Docker, Kubernetes Consumption and cloud: Power BI, React.js; Microsoft Azure Primary duties will include: Discover and translate: run discovery sessions with business stakeholders; turn loosely defined asks into clear requirements, data contracts and acceptance criteria; play your understanding back in plain language. Map: produce rigorous source-to-target mappings (STTM) covering field-level lineage, transformation logic, keys, defaults, SCD handling and data quality rules, signed off by engineers and business users alike. Build: design and build batch and streaming pipelines on Databricks (PySpark, SQL, Delta Lake, Unity Catalog, Lakeflow) that follow