FanCode
Website:
fancode.com
Job details:
FanCode is India's premier digital sports destination, dedicated to delivering a best-in-class experience for fans across live and non-live sports. Launched in March 2019 by industry veterans Yannick Colaco and Prasana Krishnan, FanCode has reached over 160 million users. The platform features a wide array of global and domestic sporting leagues, in partnership with leading sports associations. FanCode offers interactive live streaming through industry-first subscription models, including Match, Bundle, and Tour Passes, as well as monthly and annual plans at accessible price points. Some of the marquee properties include La Liga, Formula 1, MotoGP, and cricket leagues from around the globe including CPL and Super Smash.
Dream Sports, India's leading Sports Technology company, is the parent company of FanCode, with brands such as Dream11 and DreamSetGo in its portfolio.
Role Overview
Technology @ FanCode
The mission is to build a platform for all sports fans — covering Live Video Streaming, Live Scores & Commentary, Video On Demand, Player Analytics, Fantasy Research, and News.
Our data infrastructure is hosted on GCP and is built around Kafka, Apache Spark, Flink, BigQuery, GCS, and Airflow/Cloud Composer — processing billions of events generated by 160M+ users across live and non-live sporting moments. We also operate data tooling on AWS (Athena) as part of a multi-cloud data foundation.
As a Data Engineer at SDE3, you will be a core technical pillar of the Data Engineering team. You will architect the next generation of FanCode's data platform — setting the standards for how data is ingested, modelled, stored, and served across the entire organisation. Beyond building, you will drive cross-team data enablement, own the data infrastructure roadmap, and mentor the engineers around you. You will be the person other engineering teams turn to when they need to build reliable, scalable, data-driven capabilities.
What you'll do
- Own the data platform architecture: Design and evolve FanCode's end-to-end data infrastructure — from real-time event ingestion through Kafka and Flink, to batch processing on Spark, to storage and serving on BigQuery and GCS
- Define engineering standards: Establish and evangelise data engineering best practices across the org — pipeline design patterns, data modelling conventions, SLAs, data quality contracts, and observability standards
- Enable the organisation with data: Lead the design of self-serve data infrastructure that allows product, ML, and analytics teams to access, query, and build on data without bottlenecks — reducing dependency on the data engineering team for routine access
- Drive cross-functional data ownership: Work with product, platform, and ML engineering to define clear data ownership boundaries, intake processes, and data contracts across teams
- Architect for scale and reliability: Build systems that are resilient to massive event spikes — live sports moments drive sudden, unpredictable bursts of user activity that the data stack must absorb without degradation
- Pipeline quality and observability: Own the observability layer for the data platform — data freshness monitoring, anomaly detection, pipeline SLO tracking, and incident management
- Cost architecture: Proactively identify and implement cost optimisation opportunities across BigQuery, Spark compute, and storage — without compromising latency or reliability
- Technical leadership and mentoring: Drive architecture reviews, set a high technical bar in code and design reviews, and actively mentor SDE1/SDE2 engineers on distributed systems, data modelling, and engineering craft
Must haves
- 7+ years of hands-on data engineering experience, with demonstrated ownership of large-scale production data infrastructure
- Deep expertise in Apache Spark and Kafka — architecture, tuning, and operating them at production scale
- Strong experience with Apache Flink for stateful stream processing
- Strong proficiency in Python; comfort reading and writing Java or Scala code is a plus
- Deep working knowledge of BigQuery or a comparable cloud data warehouse at scale — schema design, partitioning, clustering, cost governance
- Experience designing and operating multi-cloud or cloud-native data platforms (GCP and/or AWS)
- Experience with Airflow/Cloud Composer or equivalent orchestration at scale — including managing DAG complexity, dependency management, and operational reliability
- Strong data modelling skills — dimensional modelling, event schemas, slowly changing dimensions, data contracts
- Demonstrated ability to drive cross-team technical alignment — working across engineering, product, and analytics stakeholders
- Track record of setting engineering standards and leading design reviews, not just implementing features
Good to have:
- Experience building internal data platforms or self-serve data access tooling
- Familiarity with data quality frameworks, data observability tools (Monte Carlo, Great Expectations, or equivalent), or data catalogue tooling
- Experience with AWS data tooling: Athena, Glue, S3-based lake architectures
- Exposure to feature store design or ML data pipeline architecture
- Experience implementing SRE practices on data systems — SLOs/SLIs, chaos testing, runbooks
- Familiarity with GenAI or LLM data pipelines (vector stores, embedding pipelines, retrieval-augmented generation data flows)
- Industry certifications: GCP Professional Data Engineer or equivalent
- Passion for sports is a bonus
Dream Sports, India's leading sports technology company, powers the fan journey for 300 million sports fans across entertainment, content, gaming, travel, AI, and grassroot sports development. Its portfolio includes Dream11, FanCode, DreamSetGo, Dream Cricket, Dream Horizon, Dream Sports AI, and Dream Money. The company is also home to the Dream Sports Foundation, its philanthropic arm. Founded in 2008 by Harsh Jain and Bhavit Sheth, Dream Sports is headquartered in Mumbai.
Checked out Dream Locker Room yet? Head over to our official blog to get a glimpse into our culture, and how we 'Make Sports Better', together.
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