Sr Data Engineer - Python, BigQuery and DBT

Job Summary

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.
• 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.