Data Engineer, Data Platforms

S&P Global Market Intelligence; Hyderabad, Chennai; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

S&P Global Market Intelligence is seeking an experienced Data Engineer to lead the transition of data pipelines into a robust enterprise platform. You will stabilize pipelines, implement reusable patterns, and ensure secure, governed data access using AWS services and lakehouse technologies.

Responsibility includes collaboration with global teams, guidance through design reviews, and leveraging AI tools to accelerate development while maintaining security and quality standards.

Qualifications

• 5-7+ years in data engineering or related domains.
• Hands-on experience with batch and/or streaming pipelines.
• Experience with Databricks-based platforms and lakehouse architectures.
• Knowledge of AWS data services (S3, Glue, Lambda, Lake Formation, Kinesis, IAM).
• Understanding of data quality, metadata, access control, and encryption.

Responsibilities

• Transition pipelines to enterprise data platform with Databricks.
• Stabilize and optimize pipelines for performance and reliability.
• Implement repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring.
• Support secure AWS deployment and governed data access.
• Work with lakehouse tech and metadata-driven design.
• Establish observability including logs, telemetry, and dashboards.
• Provide technical guidance through reviews and planning.
• Collaborate with offshore and global teams across time zones.
• Use AI tools to accelerate engineering tasks.
• Follow responsible AI usage.

Skills

Data pipelines

Databricks

AWS services

Delta Lake

CI/CD

Data quality

Communication

Team collaboration

AI tooling

Data engineering

Tools

Databricks Unity Catalog

AWS Glue

Kinesis

Delta Lake

Job description

Job Summary

About the Role: Grade Level (for internal use): 10

Responsibilities

Data Pipeline Transition and Platform Delivery

• Partner with Databricks and platform teams to transition existing pipelines into the target enterprise data platform.
• Provide hands-on engineering support to stabilize and optimize pipelines post-transition, focusing on performance, scalability, maintainability, and operational reliability.
• Implement and follow repeatable engineering patterns for ingestion, transformation, testing, deployment, and monitoring.
• Support secure AWS deployment patterns and governed data access using AWS IAM, encryption, and platform guardrails.
• Work with lakehouse technologies such as Delta Lake, Apache Iceberg, and Databricks Unity Catalog, and support metadata-driven pipeline design.
• Assist with establishing and maintaining observability practices (logging, telemetry, alerts, dashboards) for production pipelines.

Data Onboarding and Asset-Agnostic Enablement

• Support asset-agnostic onboarding by applying standard ingestion and transformation frameworks to new datasets.
• Help offshore teams adopt common onboarding standards, delivery practices, and technical templates.
• Contribute to reusable technical assets such as reference implementations, onboarding templates, documentation, and operational runbooks.
• Support both batch and streaming onboarding patterns using AWS services (e.g., AWS Glue, AWS Lambda, Amazon Kinesis) or event-driven integrations as appropriate.

Data Mastering Platform Integration

• Support integration of platform datasets with the enterprise data mastering platform.
• Help establish and/or maintain data quality controls, reconciliation, and metadata alignment for trusted mastered data.
• Support golden record / golden copy generation and maintenance activities for key financial datasets (as applicable to the program).

Technical Leadership and Engineering Excellence

• Provide technical guidance to engineering peers through code reviews, design reviews, and implementation planning.
• Contribute to engineering standards and delivery rigor across CI/CD, infrastructure automation, testing practices, and operational readiness.
• Help ensure documentation quality and operational discipline (runbooks, support procedures, incident learnings).
• Act as a technical bridge between offshore execution teams and global platform stakeholders to resolve delivery blockers and keep alignment.

AI-Assisted Engineering and Context Engineering

• Use AI tools (Claude, GitHub CoPilot, LLMs) to accelerate engineering tasks such as drafting code, refactoring, writing documentation, generating unit tests, and troubleshooting.
• Apply basic prompt engineering and context engineering practices by providing the right technical background, constraints, schemas, examples, and acceptance criteria to improve output quality.
• Validate and verify AI-generated outputs before adoption, especially for correctness, security, and maintainability.
• Follow responsible AI usage expectations, including protection of sensitive data, credentials, and regulated information.

Qualifications

Technical Experience

• 5-7+ years of experience in data engineering, data platforms, or related technical domains.
• Hands-on experience building and operating batch and/or streaming data pipelines.
• Experience working with Databricks-based platforms and lakehouse architectures.
• Strong familiarity with AWS data engineering services such as Amazon S3, AWS Glue, AWS Lambda, AWS Lake Formation, Amazon Kinesis, and AWS IAM.
• Working knowledge of Delta Lake, Apache Iceberg, and Databricks Unity Catalog (or equivalent governed lakehouse concepts).
• Solid understanding of data quality, schema management, lineage/metadata basics, access control, encryption, and operational support.

Leadership and Collaboration

• Ability to provide technical guidance without people management responsibility.
• Experience collaborating with global teams across time zones and working through ambiguous delivery problems.
• Strong communication skills and ability to translate technical requirements into implementable tasks.

AI Tooling (Required)

• Practical experience using AI-assisted engineering tools such as Claude, GitHub CoPilot, or LLM-based development features.
• Ability to use AI effectively with basic context engineering practices (inputs/constraints/examples/acceptance criteria).
• Demonstrated ability to validate AI-generated outputs and follow responsible, secure usage practices.

Preferred Qualifications

• Experience with enterprise onboarding frameworks, reusable ingestion patterns, and pipeline templates.
• Familiarity with data cataloging/metadata management and governed data product concepts.
• Knowledge of semantic modeling (e.g., semantic layers, business definitions, canonical models) is a plus.
• Exposure to enterprise data mastering/MDM workflows, golden record concepts, and trusted data distribution.
• Experience in regulated environments with strong governance, auditability, and security expectations.
• Databricks and/or AWS certifications (or equivalent practical experience) is a plus.