Role Summary As a Data Engineer in the Lakehouse and AI Data Platform team you will design build test and support data pipelines and curated datasets on the firms modern data platform You will work across ingestion transformation modelling optimisation and data quality helping to deliver data products that are reliable scalable and fit for purpose Where there are gaps in platform functionality you may also contribute to shared tooling or framework components that improve how the platform is used and operated The role is suited to engineers who are comfortable writing code working with SQL and distributed data processing and solving practical delivery problems in a team environment More experienced candidates may also contribute to technical design platform standards and the shaping of delivery approaches across a wider set of use cases Key Responsibilities Pipeline Engineering Build enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform Refactor or modernise existing data flows where needed to improve reliability performance and maintainability Where needed build reusable tooling to improve delivery consistency and operational support Ensure data pipelines are productionready well tested and operationally supportable Data Modelling and Curation Develop raw refined and curated datasets that support analytics reporting and AI use cases Apply sound data modelling principles to represent business entities relationships and historical change accurately Work with consumers to shape data products that are usable well documented and aligned to business needs Data Quality and Reconciliation Implement controls to validate completeness accuracy and consistency of data across pipelines and datasets Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise Contribute to clear standards for testing monitoring and issue resolution Contribute to practical improvements in testing monitoring or reconciliation tooling where these strengthen platform reliability and daytoday delivery Delivery and Partnership Work closely with engineers platform teams and data consumers to deliver agreed outcomes to time and quality expectations Communicate clearly on progress risks dependencies and design choices including where delivery would benefit from improvements to shared platform tooling For more senior candidates take a broader role in technical leadership task breakdown and support for junior engineers Skills and Experience Required 712 years of experienceBachelors or masters degree in a relevant discipline or equivalent practical experience with evidence of strong quantitative skills or data engineering expertise Strong handson programming experience in Python or Java Good working knowledge of SQL including troubleshooting optimization and data analysis Ability to learn new tools internal platforms and delivery workflows quickly Familiarity with software engineering fundamentals including version control testing release discipline and CI CD practices Data Engineering Capability Understanding of temporal data modelling including the handling of historical state and change over time Knowledge of schema design schema evolution and data compatibility considerations Understanding of partitioning clustering and other techniques used to improve data performance at scale Ability to make sensible design choices across normalized and denormalized models and between natural and surrogate keys Practical approach to data quality reconciliation and rootcause analysis Experience building or supporting production data pipelines in a collaborative engineering environment Experience working with distributed data processing frameworks such as Apache Spark Working knowledge of common data formats such as JSON Avro and Parquet Stronger ownership of technical design across multiple datasets or pipeline domains Experience guiding implementation standards code quality and engineering practices within a team Ability to lead delivery for a workstream manage dependencies and support less experienced engineers Technology Workplace The role will involve working with a modern and evolving data stack Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies Examples of technologies in scope include Data processing and logic ANSI SQL Apache Spark KafkaData formats JSON Avro ParquetPlatforms and storage Snowflake Apache Iceberg Databricks Hadoop ecosystem technologies Sybase IQEngineering and deployment CI CD tooling containerized or Kubernetesbased deployment approaches where relevant You will also work with internal data management and platform tooling so a practical and adaptable engineering mindset is important What We Are Looking For We are

📌 Software Engineering Data Lakehouse and AI Data Platform (Bengaluru)
🏢 Goldman Sachs
📍 Bengaluru