Senior Data Engineer – Python, BigQuery and DBT

Overview:

We are looking for a Senior Data Engineer Python BigQuery and DBT to join our Index Engineering team in Gurgaon. You will be part of a team that builds and evolves critical data platforms on a modern cloud-native stack using dbt BigQuery and Apache Airflow on Google Cloud Platform. You will work with large-scale financial datasets including securities master data corporate actions and market data from external vendors designing robust ELT pipelines improving data models and driving platform enhancements using modern engineering practices. This is a hands-on role for someone with strong data engineering fundamentals someone who understands how to design scalable pipelines model data effectively and build reliable systems that serve business-critical workloads.

Responsibilities:

Data Modelling & Transformation

• Design and build data models that accurately represent business domains applying dimensional modelling slowly changing dimensions and normalisation/denormalisation trade-offs appropriate to the use case.

• 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.

• Bachelors or Masters 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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  #MIDSENIOR
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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 4000 professionals operating across 20 countries and serving approximately 5000 clients including many of the worlds 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.

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ISS STOXX is committed to fostering cultivating and preserving a culture of diversity and inclusion. It is our policy to prohibit discrimination or harassment against any applicant or employee on the basis of race color ethnicity creed religion sex age height weight citizenship status national origin social origin sexual orientation gender identity or gender expression pregnancy status marital status familial status mental or physical disability veteran status military service or status genetic information or any other characteristic protected by law (referred to as protected status). All activities including but not limited to recruiting and hiring recruitment advertising promotions performance appraisals training job assignments compensation demotions transfers terminations (including layoffs) benefits and other terms conditions and privileges of employment are and will be administered on a non-discriminatory basis consistent with all applicable federal state and local requirements.