About the role

The Senior Data Engineer will architect and maintain a Snowflake-based Data Lakehouse, developing robust ETL/ELT pipelines to support business intelligence and regulatory reporting. They will also collaborate with cross-functional teams to ensure data integrity, governance, and high-performance analytical solutions.

What they look for

Snowflake Data Engineering SQL dbt Power BI DAX Data Modeling ETL/ELT Data Governance Python AWS Terraform Git Data Visualization Financial Services Fintech

Requirements

Candidates must have 5-8 years of experience in data engineering, with proven expertise in Snowflake, SQL, dbt, and Power BI within financial or trading environments. A degree in Computer Science or a related field is required, along with strong communication skills to translate business needs into technical architecture.

Full description

Overview

EC Markets is building a next-generation data platform to power trading, operational, and marketing intelligence. We are seeking a Senior Data Engineer to drive evolution of our Snowflake-based Data Lakehouse, establishing a modern data ecosystem that supports advanced analytics, compliance, and decision-making across the business.

This is a high-impact role for an experienced engineer with a track record of architecting and implementing scalable data platforms — ideally in financial or trading environments — who can talk and think data from ingestion to MI dashboards.

Key Responsibilities

Architecture & Development

Assume ownership of a Snowflake-centric Data Lakehouse integrating structured, semi-structured, and unstructured data.

Develop and support robust ETL/ELT pipelines that ingest and transform data from multiple internal systems (trading, CRM, finance, risk, etc.) and external APIs.

Implement data models, schemas, and transformation frameworks optimised for analytical and regulatory use cases.

Apply best practices in data versioning, orchestration, and automation using modern data engineering tools.

Ensure scalability, data lineage, and governance across the data lifecycle.

Reports and data visualisation

Build and own semantic models on top of Snowflake (DirectQuery and Import), using DAX, calculation groups, RLS/OLS, and incremental refresh.

Develop operational reports, dashboards and data extracts

Data Governance & Quality

Maintain high data integrity, privacy, and security aligned with FCA and GDPR requirements.

Monitor and optimise query performance and storage efficiency.

Cross-Functional Collaboration

Partner with business units (Trading, Finance, Marketing, Compliance, Operations) to capture data requirements and translate them into robust technical solutions.

Support regulatory, management, and operational reporting requirements through structured data models.

Skills & Experience (Non-negotiable)

Experience in financial services, trading, or fintech environments.

Proven experience designing and delivering DWH / Delta Lakehouse using Snowflake.

Expert level SQL and data modelling expertise (star/snowflake schemas, dimension/al modelling).

2+ years writing dbt models in production. Comfortable with sources, snapshots, tests, macros, exposures, and the medallion (bronze/silver/gold) pattern.

3+ years building production Power BI on enterprise warehouses. Expert DAX (time intelligence, variables, virtual relationships, calculation groups), Power Query / M, Tabular Editor, DAX Studio.

Strong SQL on Snowflake. Understand warehouse sizing, clustering, query profiles, and the cost levers that matter.

Familiarity with orchestration and transformation frameworks.

Hands-on experience with data analysis, visualisation, and operational reporting tools.

Excellent communication skills and ability to translate business requirements into scalable data architecture.

Skills & Experience (nice to have)

Ability to create scripts in Python or another scripting language.

Experience with AWS (ECS, S3, IAM), Terraform, Git/GitHub Actions.

Power BI embedded, Fabric, or a credible opinion on when not to use them.

Exposure to Microsoft Fabric Direct Lake, Snowflake Cortex / Claude.ai connector, or other AI-on-warehouse patterns.

Qualifications

Degree in Computer Science, Data Engineering, or related field.

5–8 years of hands-on experience in data engineering and / or analysis.