IND Lead Software Engineer

IND Lead Software Engineer - GCC026
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Job Description: Senior Data Engineer (Analytics Engineering)
Technology Strategy & Enablement (TS&E) | Hyderabad, India
Location
Hyderabad, India
Employment Type
Full-Time
Experience Level
5-8 Years
Organization
Technology Strategy & Enablement (TS&E)
Position Overview
We are seeking a Senior Data Engineer with strong analytics and data science capabilities to join TS&E. The engineer will architect connected data systems across AWS and GCP, build trusted analytical foundations, and connect workforce, AI adoption, engineering, delivery, and financial information for executive reporting and decision support.
Key Responsibilities
Data Platform Architecture
• Design distributed data architectures spanning AWS and GCP, including ingestion, storage, processing, analytics, vector retrieval, and AI-serving patterns.
• Architect solutions across relational databases, non-relational databases, data lakes, data warehouses, and vector databases based on workload requirements.
• Design and support AWS services including S3, Glue, Redshift, Athena, Lake Formation, Lambda, and Step Functions.
• Apply data warehouse concepts, dimensional modeling, and star schemas to reusable analytical structures.
• Design interfaces between enterprise data, vector stores, GCP AI services, and executive-facing applications.
Data Engineering & Integration
• Design AWS DocumentDB models and organizational relationships across LOB, Value Stream, ART, and Team.
• Build resilient Python, Lambda, API, file, event-driven, and orchestrated ingestion and transformation pipelines.
• Integrate Rally, GitHub, GitHub Copilot, Clarity, Workday, Apptio, and related systems.
• Preserve lineage, source identifiers, refresh metadata, security controls, and operational observability.
Analytics & Data Science
• Translate leadership questions into measurable analyses, datasets, and outputs.
• Perform trend, correlation, segmentation, comparative, and attribution analysis.
• Create transparent analytical logic for patterns, exceptions, and areas to amplify, investigate, or validate.
• Prepare trusted datasets for Power BI, Tableau, QuickSight, and AI-generated executive explanations.
• Document assumptions, populations, exclusions, limitations, and metric definitions.
Data Governance & Quality
• Establish lineage, ownership, data contracts, validation rules, quality controls, and service expectations.
• Partner with Quality Engineering on reconciliation, automated test coverage, and release readiness.
• Monitor completeness, freshness, join coverage, schema change, pipeline health, and cross-cloud reliability.
Required Qualifications & Skillset
• 5-8 years in data engineering, analytics engineering, or a related role.
• Strong Python and advanced SQL skills for integration, transformation, analysis, and automation.
• Production experience with relational and non-relational databases, data lakes, data warehouses, and vector databases.
• Hands-on AWS experience with S3, Glue, Redshift, Athena, Lake Formation, Lambda, and Step Functions.
• AWS DocumentDB/MongoDB experience including JSON modeling, indexing, and aggregation pipelines.
• Experience designing distributed and cross-cloud data architecture across AWS and GCP.
• Strong data warehouse design, dimensional modeling, and star-schema skills.
• Experience integrating enterprise SaaS systems via APIs, files, or managed interfaces.
• Practical statistical analysis, trend, correlation, segmentation, attribution, and visualization experience.
Preferred Qualifications
• Pandas, NumPy, SciPy, scikit-learn, PySpark, or comparable analytical libraries.
• Experience with GCP data and AI services, vector retrieval, and grounded generative AI applications.
• Power BI, Tableau, QuickSight, or comparable visualization tools.
• CI/CD, infrastructure as code, and automated deployment of data services.
• Rally, Clarity, Workday, Apptio, GitHub, or GitHub Copilot experience.
• Regulated enterprise data governance, privacy, security, or audit experience.
Success Profile
• Architects maintainable connected data products across AWS and GCP.
• Moves from acquisition and modeling through analysis and clear interpretation.
• Balances scalability, analytical depth, transparency, security, and business usefulness.
• Owns data quality, lineage, operational reliability, and production outcomes.
• Communicates clearly across distributed US and Hyderabad teams.
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