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About the role : We are looking for a Senior Data Engineer to own the data foundations underpinning our sports intelligence products. You will be responsible for designing and evolving the canonical data model across multiple sports and data sources, building reliable data warehouses and pipelines, and ensuring that the data consumed by analytics, modelling, and engineering teams is accurate, scalable, and well governed. A key part of this role will involve working with large historical archives and messy multi-source datasets solving challenges around entity resolution, deduplication, reconciliation, and data quality. You will also help set technical direction and raise engineering standards as the data team grows. Key Requirement: Data Architecture & Modelling Design and own the canonical data model underpinning sports intelligence products across multiple sports and data sources. Build and maintain the data warehouse and downstream data marts supporting analytics and modelling workloads. Design durable and scalable data structures that can support evolving product and business requirements. Apply sound judgement around abstraction and generalisation, balancing future flexibility with practical implementation. Partner closely with Product and Modelling teams to translate requirements into robust data structures. Data Migration & Consolidation Own migration and consolidation of large historical data archives onto new data models. Design processes for entity resolution and deduplication across multiple data sources, datasets, and historical eras. Establish reconciliation frameworks to ensure consistency and accuracy during data migration. Identify and resolve data inconsistencies across complex and heterogeneous source systems. Data Quality & Governance Define and enforce data quality standards across the data ecosystem. Establish validation, reconciliation, and monitoring processes to ensure data accuracy and reliability. Identify data quality issues proactively and drive their resolution. Establish clear standards for data models, schemas, documentation, and data interfaces. Data Pipelines & Infrastructure Build and operate reliable, scalable data pipelines across live and batch data sources. Ensure pipelines are well monitored, observable, and resilient in production. Work with modern data warehousing, orchestration, and cloud infrastructure technologies. Continuously improve pipeline reliability, performance, and maintainability. Data Interfaces & Engineering Standards Own versioned data interfaces consumed by other engineering teams. Define and maintain schema contracts, documentation, and deprecation policies. Ensure changes to data structures are managed in a controlled and transparent manner. Contribute to architecture and technical decisions across the data engineering function. Set technical direction and raise engineering standards across a growing data team. Background and Experience: Required 5+ years of experience in Data Engineering, with significant experience owning data models and platforms that have evolved beyond their original use case. Strong expertise in dimensional modelling and data warehouse design. Expert-level SQL skills. Strong proficiency in Python. Demonstrated experience handling entity resolution, deduplication, and data quality at scale across complex, multi-source datasets. Experience designing, managing, and versioning schemas and data interfaces consumed by other engineering teams. Experience with modern data warehousing and orchestration technologies. Strong understanding of cloud-based data infrastructure. Experience building and operating reliable data pipelines across batch and/or live data sources. Ability to work independently within a small team and make sound technical decisions with limited supervision. Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field. Good to Have Experience working with sports data, particularly cricket data. Experience with streaming and event-driven data systems. Experience working within a platform or infrastructure team serving internal data consumers. Exposure to feature stores or data serving layers supporting analytics and ML workloads. Experience working with large-scale historical datasets or complex data archives. The Edge: You know that a great intelligence product starts with great data and you know how to build the foundation to make it last. What We Offer: Global Exposure: Work with marquee sports properties across 40+ sports worldwide. Innovative Culture: A "fan-first" environment that values curiosity, quality, and fun. Flexibility: Monday to Friday schedule with flexible timings. Job Snapshot Updated Date 11-08-2026 Job ID Job_203041 Department FanOS Intelligence - Technology Location Goregaon, Mumbai, Maharashtra, India Experience 5 - 8 Years Employee Type Full Time .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.