Data Engineer - Sr Analyst

Summary

Position Summary Sr. Analyst – Data Engineer – Deloitte Support Services India Private Limited Work you’ll do: We are seeking a seasoned Data Engineer to design, build, and operate scalable data pipelines and curated datasets that power analytics, reporting, and data products. This role requires strong hands-on data engineering, data modeling, and data architecture experience in cloud environments (preferably GCP) and Python. Collaborate with cross-functional agile team of analytics engineers, human centered designers, web developers and dev-ops engineers to launch innovative AI-driven Data and Analytics products. Experience with Azure and AWS data engineering tools is a plus Responsibilities: ─ Build and maintain batch and/or streaming data pipelines to ingest, transform, and serve data for analytics and AI use cases ─ Enhance data collection procedures while upholding data security and privacy norms and obligations to gather most relevant data ─ Develop reusable Python components for data processing, automation, and orchestration. ─ Design and implement logical and physical data models to support BI, analytics and AI consumption. ─ Contribute to data architecture decisions: source-to-target design, layering (raw/curated/semantic), integration patterns, and scalability considerations. ─ Implement data quality checks, validation, monitoring/alerting, and production support processes. ─ Optimize performance and cost (query tuning, partitioning/clustering, pipeline efficiency). ─ Support AI-driven analytics products by enabling feature-ready datasets, metadata, lineage, and reproducible data inputs for model training/inference ─ Participate in code reviews, design reviews, documentation, and knowledge sharing. ─ Develop an understanding of the core business functions of the client, and bring innovative analytics solution ideas to support those functions Required Technical Skills: Must Have: ─ Data engineering: building production-grade ETL/ELT pipelines and managing structured datasets. ─ Data modeling: translating business needs into analytics-ready models; strong SQL. ─ Data architecture: designing end-to-end data flows, standards, and patterns for reliability and scalability. ─ GCP: hands-on experience with common services like BigQuery, Cloud Storage (GCS), Pub/Sub, Dataflow, Dataproc (Spark), Cloud Composer (Airflow), Cloud Functions and/or Cloud Run, Cloud Logging/Monitoring, Vertex AI, Secret Manager, IAM. ─ Python: strong scripting/software engineering fundamentals (testing, packaging, debugging, maintainable code). ─ Proven technical troubleshooting and performance tuning experience ─ Experience with integrating Looker, Power BI, Tableau, any other BI tools Good to have: ─ Azure data engineering tools/services (e.g., Fabric, Data Factory, Databricks). ─ AWS data engineering tools/services (e.g., Glue, Redshift, EMR, Lambda). ─ Familiarity with AI engineering concepts (feature engineering, training/inference data consistency, model monitoring data needs, governance for AI/ML data). ─ Exposure or experience supporting AI-driven data and analytics products, such as preparing ML-ready datasets, feature pipelines, or integrating data workflows into model development/serving processes. ─ Familiarity with CI/CD and Infrastructure-as-Code practices for data platforms. ─ Experience with governance concepts (catalog/lineage, access controls, PII handling) Soft Skills: ─ Excellent verbal and written communication skills, with an ability to convey complex concepts clearly and concisely. ─ Good at engaging business stakeholders and apt at business engagement and requirement discovery ─ Analytical ability to manage multiple projects and prioritize tasks into manageable work products ─ Can operate independently or with minimum supervision ─ Strong sense of ownership, urgency, and drive ─ High level of initiative and self-motivation Education: ─ Bachelor's degree, preferably in Computer Science, Information Technology, Computer Engineering, or related IT discipline; or equivalent experience Experience: ─ 3–5 years of hands-on experience in data engineering and analytics data delivery. ─ Demonstrated experience designing data models and contributing to data architecture for cloud-based platforms. ─ Experience supporting production pipelines (monitoring, troubleshooting, SLAs) and working in Agile delivery teams. ─ Prior experience partnering with data engineers, AI engineers and DevOps practitioners to operationalize data for model training and analytics products is preferred. Location: Hyderabad Shift Timing: Shift between 2:00 PM to 11:00 PM IST

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