Senior Data Platform Engineer

People on the move deserve a bank that moves with them. Since 2022, Aspora has been building a borderless financial operating system that makes money as mobile and transparent as its users.

Backed by influential venture capitalists like Sequoia Capital, Greylock Partners, Hummingbird Ventures, Y Combinator & Global Founders Capital. We're a team of 150+ across India, the UK, the UAE, EU and the US, working with extreme ownership, radical candour, and an obsession with customer impact.

We celebrate builders who question assumptions, ship fast, and turn regulatory complexity into elegant solutions. If you’re driven to redefine what global banking can be, we’d love to build the future with you.

We're building the data infrastructure that powers decisions across every part of our business — from real-time analytics to large-scale batch computation. As a Senior Data Platform Engineer, you'll own the systems that process billions of events, move data reliably, and make insights fast to produce.

You'll work closely with analytics, ML, and product engineering teams — setting the bar for reliability, performance, and data quality across the platform.

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• Design, build, and operate large-scale data processing infrastructure using Spark on Databricks — ensuring reliability, performance, and cost efficiency at scale.

• Architect and maintain lakehouse solutions (Delta Lake, Iceberg) including partitioning strategies, Z-ordering, and compaction jobs.

• Own cluster management, autoscaling policies, and resource governance across Databricks workspaces.

• Drive platform-level improvements: query optimisation, caching strategies, compute–storage separation, and shuffle tuning.

• Design and build robust, idempotent, and testable data pipelines handling batch and near-real-time workloads.

• Manage and extend our Airflow-based orchestration layer — DAG authoring standards, dependency management, alerting, and SLA enforcement.

• Implement and maintain CDC pipelines (Debezium, Kafka Connect, or native DB replication) ensuring low-latency, high-fidelity data propagation.

• Define data pipeline contracts (schemas, SLAs, quality assertions) and enforce them via automated data quality frameworks.

• Model and manage analytical data stores — dimensional models, OBT patterns, and aggregation layers optimised for BI and self-serve analytics.

• Own the evolution of our analytical warehouse/lakehouse stack — performance benchmarking, cost modelling, and technology selection.

• Build and maintain efficient data serving layers for dashboards, ML feature stores, and reverse ETL use cases.

• Implement data retention, archival, and lifecycle management policies across hot/warm/cold storage tiers.

• Define and enforce data platform engineering best practices — code standards, CI/CD for pipelines, automated testing, and observability.

• Build internal tooling and libraries that make data engineers faster: reusable Spark utilities, pipeline templates, local dev environments.

• Champion data reliability engineering: lineage tracking, incident response playbooks, pipeline SLO monitoring, and root cause analysis.

| Area | Tools | Compute | Apache Spark, Databricks, PySpark, Scala | Orchestration | Apache Airflow, dbt | Ingestion & CDC | Debezium, Kafka, Kafka Connect | Storage | Delta Lake, Iceberg, S3/GCS, Snowflake | Languages | Python, SQL, Scala | Observability | Great Expectations, OpenLineage, Monte Carlo |

• 5+ years of data engineering experience with 2+ years on large-scale big data platforms.

• Hands-on expertise with Apache Spark — performance tuning, partitioning, broadcast joins, execution plans.

• Deep Databricks experience — workspace configuration, Unity Catalog, Delta Live Tables, or equivalent.

• Solid Apache Airflow experience: DAG authoring, custom operators, XCom, Pools, and sensor patterns.

• Production experience implementing CDC pipelines (Debezium, Kafka Connect, or DMS).

• Strong proficiency in Python and SQL.

• Experience designing analytical data models for large datasets (star schema, wide tables, aggregation layers).

• Track record of building reliable, observable, and testable pipelines in production.

• Hands-on experience with modern data lake technologies like Delta Lake or Apache Iceberg, including compaction, time travel, and schema evolution

• Experience building and operating streaming data pipelines using Apache Spark Structured Streaming, Apache Flink, or Kafka Streams

• Proficiency with dbt for data transformations and lineage management

• Experience working with cloud data infrastructure on Amazon Web Services, Google Cloud Platform, or Microsoft Azure

• Familiarity with infrastructure-as-code tools such as Terraform or AWS CloudFormation

• Experience owning data platform reliability end-to-end, including monitoring, alerting, and building self-healing systems

• A strong data-as-a-product mindset, with emphasis on clear contracts, versioned schemas, SLOs, and well-documented datasets

• A bias toward automation—proactively reducing operational toil by building scalable frameworks and tooling

• Solid engineering fundamentals, including writing testable code, participating in rigorous code reviews, and maintaining high standards for operational excellence