Senior Data Engineer – Python, BigQuery and DBT

Develop and maintain data transformation logic using dbt on BigQuery — leveraging models, macros, incremental strategies, and tests to keep transformations modular, version-controlled, and well-documented.

Define and enforce naming conventions, modelling standards, and layering practices (staging, intermediate, marts) across the data warehouse.

Data Pipeline Engineering

Design, build, and maintain ELT pipelines that ingest, transform, validate, and serve financial data at scale — with a focus on reliability, idempotency, and observability.

Build ingestion frameworks for external data vendor feeds — handling diverse file formats, schema variations, validation rules, reconciliation, and error recovery.

Orchestrate pipeline workflows using Apache Airflow (Cloud Composer), managing dependencies, retries, SLAs, and alerting.

Design and implement full- and incremental-load strategies, backfill mechanisms, and pipeline-recovery patterns.

Data Quality & Reconciliation

Implement data reconciliation processes to verify accuracy across upstream sources and internal datasets — building automated checks for row counts, value matches, and business rule compliance.

Define and enforce data quality standards through automated testing, validation layers, and monitoring — treating data quality as a first-class engineering concern.

Set up monitoring and alerting for pipeline health and data freshness using tools such as Datadog.

Platform & Performance

Design partitioning, clustering, materialisation, and caching strategies to optimise query performance and manage storage costs in BigQuery.

Build and support RESTful APIs (FastAPI) for internal and external data consumption.

Support and improve CI/CD pipelines for data platform components.

Participate in disaster recovery planning, testing, and documentation for data infrastructure.

Collaboration & Continuous Improvement

Collaborate with operations, product, and other engineering teams to translate business requirements into well-designed technical solutions.

Establish and maintain data lineage, documentation, and cataloguing practices so that pipelines and models are understandable and auditable.

Explore and apply Generative AI capabilities (e.g., LLM-based tooling, RAG patterns) to improve engineering workflows, documentation, and developer productivity.

Troubleshoot production data issues, perform root-cause analysis, and implement fixes with a sense of urgency.

Qualifications:

Financial services or fintech domain experience — particularly in securities master data, corporate actions, index calculations, or market data vendor feeds.

Experience with data platform modernisation — rebuilding legacy pipelines using modern ELT approaches.

Understanding of exchange calendars, business day logic, and how they affect data processing schedules.

9+ years of experience in data engineering, database development, or a related role.

Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field.

Technical Skills:

Data Modelling & SQL

Strong data modelling skills — dimensional modelling, star/snowflake schemas, slowly changing dimensions, and the ability to design models that balance analytical performance with maintainability.

Deep SQL expertise — complex queries, window functions, CTEs, recursive queries, query plan analysis, and performance tuning on large datasets.

Good understanding of data formats (Parquet, Avro, JSON, CSV), serialisation trade-offs, and working with structured and semi-structured data.

Modern Data Stack

Hands-on experience with dbt — modelling, transformations, tests, documentation, macros, and incremental models.

Experience with a cloud data warehouse — BigQuery preferred, or Snowflake/Redshift with willingness to work on BigQuery.

Experience building data pipelines using Python and a workflow orchestration tool such as Apache Airflow or Cloud Composer.

Solid understanding of ELT/ETL design patterns — full vs. incremental loads, idempotent pipelines, backfill strategies, and dependency management.

Data Engineering Fundamentals

Good understanding of data lake and data warehouse architectures, and lakehouse concepts.

Experience with data reconciliation — building validation frameworks that compare data across sources and flag discrepancies.

Understanding of data governance principles — lineage, cataloguing, access control, and data quality management.

Familiarity with version control (Git) and CI/CD practices.

Cloud & Infrastructure (Nice to Have)

Experience with Google Cloud Platform services beyond BigQuery — Cloud Run, Cloud Composer, Cloud SQL, Cloud Storage.

Experience building or working with REST APIs (FastAPI, Flask, or similar).

Familiarity with API gateway platforms such as Apigee.

Experience with monitoring and observability tools such as Datadog.

Knowledge of relational databases such as SQL Server or PostgreSQL — including stored procedures, indexing, and query execution plans.

Familiarity with legacy ETL tools (SSIS, Informatica, or similar).

Awareness of Generative AI concepts — large language models, retrieval-augmented generation (RAG), agentic AI patterns — and interest in applying them to data engineering and automation use cases.

Familiarity with change data capture (CDC) and event-driven data patterns.

Soft Skills

You think in terms of data flows, dependencies, and failure modes — not just code that works today.

Strong ownership mindset — you take responsibility for what you build and see issues through to resolution.

Clear and direct communication with both technical and non-technical stakeholders.

Strong problem-solving skills and ability to work independently in a fast-paced environment.

Curious to learn financial domain concepts and apply them to engineering decisions.

Comfortable working in a globally distributed team across time zones.

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What You Can Expect from Us

Our people are the moving force behind our work. We are committed to building a culture that values diverse skills, perspectives, and experiences. If you have the skills, passion, and drive to help us bring clarity and transparency to capital markets, we want to work with you.

Together, we can grow your career in an environment that fuels creativity, drives innovation, and has real impact.

About ISS STOXX

ISS STOXX delivers world-class research, data, and technology solutions that empower capital market participants to pursue their visions with confidence. Our expertise spans indices, corporate governance, sustainability, cyber risk, and fund intelligence, giving clients the tools they need to uncover opportunities, manage risks, and navigate evolving regulations. We are made up of 4,000 professionals operating across 20 countries and serving approximately 5,000 clients, including many of the world’s leading institutional investors. Our scale and reach give us deep market knowledge, while our innovative methodologies allow us to offer our clients tailored insights that drive impact and success.

ISS STOXX Indices, including the renowned STOXX and DAX families, sets the standard for rules-based, transparent, and liquid benchmarks and indices. Our ready-made products and customized solutions are used by sophisticated institutional investors, ETF issuers, and providers of derivatives and structured products.

Visit our website: https://www.iss-stoxx.com/

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