Senior Data Engineer – Python & SQL

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.

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.

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.

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.

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.

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.

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.

At ISS STOXX, our people are our driving force. We are committed to building a culture that values diverse skills, perspectives, and experiences. We hire the best talent in our industry and empower them with the resources, support, and opportunities to grow—professionally and personally.

Together, we foster an environment that fuels creativity, drives innovation, and shapes our future success.

Let’s empower, collaborate, and inspire.

Let’s be #BrilliantTogether.

ISS STOXX GmbH is a leading provider of research and technology solutions for the financial market. Established in 1985, we offer top-notch benchmark and custom indices globally, helping clients identify investment opportunities and manage portfolio risks. Our services cover corporate governance, sustainability, cyber risk, and fund intelligence. Majority-owned by Deutsche Börse Group, ISS STOXX has over 3,400 professionals in 33 locations worldwide, serving around 6,400 clients, including institutional investors and companies focused on ESG, cyber, and governance risk. Clients trust our expertise to make informed decisions for their stakeholders' benefit.

STOXX® and DAX® indices comprise a global and comprehensive family of more than 17,000 strictly rules -based and transparent indices. Best known for the leading European equity indices EURO STOXX 50®, STOXX® Europe 600 and DAX®, the portfolio of index solutions consists of total market, benchmark, blue-chip, sustainability, thematic and factor-based indices covering a complete set of world , regional and country markets. STOXX and DAX indices are licensed to more than 550 companies around the world for benchmarking purposes and as underlyings for ETFs, futures and options, structured products, and passively managed investment funds. STOXX Ltd., part of the ISS STOXX group of companies, is the administrator of the STOXX and DAX indices under the European Benchmark Regulation.

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