Lead Software Engineer - Data Engineering

JPMorgan Chase & Co.; Greater London; On-site; GBP 80,000 - 100,000; Full time

Job summary

A leading financial services firm is seeking a Senior Lead Engineer to design and develop scalable data architectures. You will be responsible for implementing data pipelines while ensuring compliance with regulations. The ideal candidate will have strong programming skills, experience with cloud services, and a collaborative mindset. Join a team that values diversity and partnership as you contribute to innovative solutions in the fintech space.

Qualifications

• Experience with programming in Python and at least one JVM language.
• Expertise in SQL including joins and aggregations.
• Ability to design and maintain data pipelines.
• Hands-on experience with Kafka and cloud providers.
• Familiarity with data warehousing technologies.

Responsibilities

• Design and develop scalable data architectures.
• Implement and maintain efficient data pipelines.
• Ensure compliance with data privacy regulations.

Skills

Python

Java

SQL

Kafka

ETL/ELT pipelines

GCP

AWS

Data modelling

Tools

Spark

Snowflake

Databricks

Airflow

Job description

Out of the successful launch of Chase UK in 2021, we’re a new team, with a new mission. We’re creating products that solve real world problems and put customers at the center - all in an environment that nurtures skills and helps you realize your potential. Our team is key to our success. We’re people-first. We value collaboration, curiosity and commitment.

As a hands-on Senior Lead Engineer at JPMorgan Chase within the International Consumer Bank, you are the heart of this venture, focused on getting smart ideas into the hands of our customers. You have a curious mindset, thrive in collaborative squads, and are passionate about new technology. By your nature, you are also solution-oriented, commercially savvy and have a head for fintech. You thrive in working in tribes and squads that focus on specific products and projects – and depending on your strengths and interests, you'll have the opportunity to move between them.

While we’re looking for professional skills, culture is just as important to us. We understand that everyone's unique – and that diversity of thought, experience and background is what makes a good team, great. By bringing people with different points of view together, we can represent everyone and truly reflect the communities we serve. This way, there's scope for you to make a huge difference – on us as a company, and on our clients and business partners around the world.

Job responsibilities

• Architecture and implementation: Design and develop scalable and secure distributed architectures and solutions, focusing on data ingestion and processing - utilising appropriate cloud native technologies and services.
• Data pipeline development: Design, implement, and maintain data pipelines that efficiently collect, process, and store large volumes of data from various sources, ensuring data timeliness, quality, and completeness.
• Security and compliance: Ensure that data solutions comply with relevant data residency and privacy regulations, and implement best practices for securing data at rest and in transit in compliance with financial regulations and firm wide policies.

Required qualifications, capabilities, and skills

• Programming: Comfortable with Python and at least one JVM language (Java/Kotlin/Scala) including sound testing and code review practices.
• SQL expertise: Joins, aggregations, subqueries, window functions
• Data pipelines: Design, build, and optimise production ETL/ELT pipelines (batch + streaming) using a popular framework (Spark, Flink, Dataflow, etc).
• Streaming: Hands-on with Kafka (topics, keys, partitions, consumer groups) at-least-once semantics, and schema registry basics.
• Warehousing/lakehouse: Data modelling, partitioning, clustering. Hands-on with one of BigQuery, Snowflake, Databricks, etc, and cloud storage or HDFSli>
• Cloud: Production experience with at least one major cloud provider (GCP/AWS) using native data services and IAM basics. FinOps-aware with cost-effective design.
• Reliability: Data quality checks, backfills, incorporating SLIs with observability and reporting.
• Kafka Connect (sources/sinks), change data capture (CDC), and schema evolution strategies.
• Orchestrators (Airflow/Dagster/Flyte/Prefect/Argo Workflows) and workflow patterns (dependencies, idempotency, retries, SLAs).
• Lakehouse platforms and table formats (Delta/Iceberg/Hudi/Avro/Parquet) and time-travel.
• Security/RBAC, PII handling, and governance basics.

Preferred qualifications, capabilities, and skills

• AWS/GCP Certifications