Senior Data Engineer

Please note that we will only be able to accept candidates who have the appropriate rights and documentation for employment in India.

Who we are.

Axi is a leading global provider of margin and deliverable Foreign Exchange, Contracts for Difference (CFDs), and Financial Spread betting. Our business has evolved into a world-class, multifaceted brokerage with offices in six regions. With heavy investment in the latest trading technology, Axi seeks to offer the most comprehensive end-to-end trading experience available, servicing traders of all levels from beginners to institutional-level clients.

Let's talk about the cool stuff you do at Axi!

As the Senior Data Engineer for the Customer Data Platform, you will play a pivotal role improving Axi’s data capabilities which deliver positive performance impacts on our global trading products. You will sit within our Product Data Team and proactively collaborate with broader business teams across Product, IT, Sales/Marketing and Finance. You will implement data initiatives which buildAxi’s utilisation of our Customer Data Platform to deliver growth strategies that increase the

adoption of Axi’s trading products that will generate new trading clients.

Your EDGE Assignment/You Will

Build and maintain ETL/ELT pipelines on Azure Databricks using PySpark or Scala to ingest, transform, and deliver data products at scale.

Ingest data from SQL Server, Cosmos DB, event streams, APIs, and external sources into cloud data lakes and Databricks lakehouses.

Design and implement data quality checks, validation rules, and monitoring to ensure reliability and early detection of anomalies.

Optimize Spark jobs for performance and cost, including cluster sizing, partitioning strategies, and caching mechanisms.

Document data pipelines, lineage, and schemas; maintain data dictionaries and metadata catalogs for consumer discovery and understanding.

Collaborate with data analysts, scientists, and domain engineers to understand requirements and refine data products.

Contribute to infrastructure and tooling for data workflows (scheduling, orchestration, monitoring, alerting).

Technical Requirements

Hands-on experience with Azure Databricks and Apache Spark (PySpark or Scala) for large-scale data processing.

Strong SQL skills, including complex queries, window functions, CTEs, and query optimization for both transactional and analytical workloads.

Experience with SQL Server: data warehousing concepts, schema design, indexing, and optimization for analytics.

Familiarity with Azure data services: Data Lake Storage, Synapse Analytics, Data Factory, or Blob Storage.

Understanding of ETL/ELT concepts, data modeling, dimensional modeling (fact/dimension tables), and slowly changing dimensions.

Experience with batch and/or stream processing concepts; familiarity with event-driven architectures.

Knowledge of data quality frameworks, testing strategies, and monitoring/alerting for data pipelines.

Basic understanding