Data Engineer - L2

We need Data Engineers who can build reliable data pipelines, ensure data correctness end-to-end, and make data usable for analytics, risk, operations, and product at scale.

Responsibilities:

• Own 1 to 2 critical data pipelines that ingest, transform, and publish production data.
• Own event tracking and ingestion pipelines (RudderStack) and the analytics warehouse that power experimentation, compliance reporting, and operational decisions.
• Design data models that are accurate, auditable, and easy to consume.
• Build reliable ingestion pipelines from transactional systems and external partners.
• Build pipelines and dashboards for compliance use cases, transaction monitoring, anomaly detection, and regulatory reporting.
• Support product experimentation and funnel analytics with correct, queryable data.
• Build and maintain real-time dashboards and alerting on key business and pipeline health metrics.
• Build and maintain reverse ETL flows to downstream operational tools (Customer.io and similar).
• Ensure data correctness and freshness through validation, monitoring, and backfills.
• Handle late arriving, duplicate, and inconsistent data safely.
• Partner with Backend, Analytics, Risk, Ops, and Product teams to translate requirements into robust data solutions.
• Improve core data metrics such as pipeline reliability, data latency, and reconciliation accuracy.
• Automate pipeline operations including retries, recovery, and backfills.

What Success Looks Like:

• Data correctness: Pipelines produce accurate and consistent data.
• Reliability: Pipelines run on time with minimal failures.
• Trust: Stakeholders rely on data for decisions.
• Operational readiness: Issues are detected before consumers are impacted.
• Decision quality: Data models support real business questions.

Requirements:

• 4 to 7 years of experience in data engineering or backend data systems.
• Strong SQL and data modelling fundamentals.
• Experience building batch and/or streaming pipelines.
• Experience working with relational databases and data warehouses or lakehouses.
• Experience handling schema evolution and backfills.
• Understanding of data quality, validation, and monitoring.
• Ability to clearly explain data tradeoffs.

Preferred (Accelerates ramp, not required):

• Experience with modern cloud warehouses or lakehouses such as BigQuery, Databricks, Snowflake, Redshift, or similar (schema design, partitioning, clustering, and query cost optimisation).
• Hands-on experience specifically with BigQuery or Databricks is a plus.
• Experience with RudderStack or equivalent event streaming tools (Segment or mParticle).
• Experience with ETL and reverse ETL workflows (Airbyte, Hightouch, Census, or similar).
• Experience with Kafka, Spark, or Flink.
• Experience with Airflow or similar orchestration tools.
• Experience with visualisation tools (Metabase, Looker, Tableau).
• Experience supporting product experimentation and funnel analytics.
• Experience in fintech or regulated domains.
• Familiarity with event-driven architectures.
• Experience mentoring junior engineers.

Our Stack:

• Warehouse / Lakehouse: Modern cloud warehouse stack (BigQuery, Databricks, Snowflake, or similar).
• Event streaming: RudderStack.
• Ingestion and orchestration: Airbyte, scheduled jobs, webhook-driven pipelines.
• Visualisation: Metabase.
• Reverse ETL: Customer.io and similar operational destinations.
• Cloud: GCP (with AWS in parts of the broader platform).