Role & responsibilities

- Define the Data Engineering strategy aligned with organizational data & AI priorities
- Drive adoption of cloud-native data platforms and modern architectures (data lakes, lakehouses, data mesh)
- Partner with enterprise teams to enable self-service data and reporting capabilities
- Oversee the design, development, and optimization of data pipelines and integrations
- Lead delivery of scalable ELT/processing workloads on Snowflake, leveraging platform-native capabilities (e.g., Snowpark) to build performant, maintainable data pipelines
- Own implementation of dbt transformation layers (models /tests/ docs) to ensure analytics-ready, version-controlled data products with repeatable deployment practices
- Ensure reliable downstream BI consumption by enabling Tableau with governed/published data sources and performance-minded design patterns for enterprise dashboards
- Drive adoption and operations of AtScale semantic models to standardize metric definitions and deliver consistent, governed analytics across BI tools and cloud warehouses
- Ensure high-quality, reliable, and scalable data infrastructure supporting AI/ML and analytics initiatives
- Manage the data platform production support team, establishing clear incident/problem response procedures, escalation paths, and post-incident learnings to protect platform reliability and business SLAs
- Collaborate with global cross-functional stakeholders to integrate data solutions into enterprise systems
- Operationalize data quality gates by embedding validations (e.g., Great Expectations suites / checks) into pipelines so issues are detected early and shared in a common, auditable format for engineering and stakeholders
- Promote innovation in data engineering through piloting recent tools, techniques, and architectures
- Lead and mentor a diverse team of Sr. Data Engineers and Data Engineers
- Foster a culture of collaboration, innovation, and continuous learning




- Build career pathways and create opportunities for team members growth in technical and leadership tracks.

Preferred candidate profile

- 9 -14 years of progressive experience, including 3-4 years of relevant leadership experience in end-to-end technical delivery and data platform support/operations for business-critical systems
- Proven experience in designing and delivering data engineering solutions at enterprise scale
- Expertise in Snowflake ELT patterns including incremental processing (Streams), orchestration (Tasks), ingestion/loading, and Snowpark (Python) for scalable transformations and pipeline automation
- Experience enabling analytics consumption via Tableau (published datasets, performance support)
- Experience delivering production transformations using dbt (models, tests, docs, CI/CD)
- Strong Snowflake data engineering skills (SQL + Snowpark), including incremental processing and operational automation using native capabilities (e.g., pipelines orchestrated with platform features)
- Proficiency in dbt for modular transformations with built-in testing and documentation practices in a production workflow
- Proficient in data visualization using Tableau or Python libraries (e.g., Matplotlib, Seaborn, Plotly) to deliver clear, actionable insights
- Experience with AtScale semantic models to standardize KPIs and improve query performance
- Strong grasp of data warehousing, lakehouse architecture, and modern analytics on cloud platforms
- Experience in any relational databases (Oracle, SQL Server, PostgreSQL) and NoSQL databases (MongoDB, Cassandra, DynamoDB)
- Strong cloud data platform expertise across Snowflake and GCP services (incl. BigQuery and Dataflow) to design and run scalable, production-grade data pipelines
- Experience with API development and microservices-based data integrations
- Proven CI/CD capability for data pipelines, enabling automated build/test/deploy workflows for data transformations and platform changes using modern DataOps practices

📌 Manager - Data Engineering (Chennai)
🏢 HD Supply
📍 Chennai