Senior Software Developer

About usTecsys is a global supply chain technology company that helps organizations achieve operational excellence through smarter supply chains. With a strong customer base across healthcare, retail, distribution, and complex logistics, we continue to grow our global footprint—and we’re excited to expand our team in India. Earlier this year, we established Tecsys Supply Chain Solutions PVT Limited in Bangalore, further strengthening our global presence. This office builds on our existing India-based support capabilities by introducing new roles and functions that are critical to our 24/7 "follow the sun" global support model. This approach allows us to better serve customers across time zones while ensuring a balanced workload for our teams around the world. Our growing India team plays a key role in supporting and enhancing our solutions, contributing to service delivery, innovation, and the ongoing success of some of the world’s most respected brands. At Tecsys, we believe in empowering our people, fostering collaboration, and building a workplace where talent thrives. Join us and be part of a globally connected team that’s transforming the future of supply chain.

Position Overview We are seeking a Senior Data Engineer to design, build, and evolve scalable data pipelines, data models, and data products on our analytics platform. This role focuses on building reliable, batch-first ETL/ELT systems on Databricks and Spark that transform structured, semi-structured, and unstructured data into high-quality, AI-consumable datasets — supporting Search, Recommendations, Marketing, and Supply Chain analytics. The ideal candidate is a hands-on engineer who can translate ambiguous business needs into production-grade data solutions, drive engineering best practices, and mentor peers while collaborating with architects, data scientists, and product teams. 

Key Responsibilities 🔹 Data Pipeline & Platform Development  • Design, build, and maintain scalable ETL/ELT pipelines that ingest and transform structured, semi-structured, and unstructured data 

• Develop high-fidelity data pipelines on Databricks/Spark optimized for reliability, cost, performance, and data freshness 

• Build curated datasets, embeddings-ready data, feature layers, and semantic abstractions that are AI/ML-consumable for downstream systems 

• Implement ingestion, transformation, and serving layers across Data Lake / Lakehouse architectures with a focus on efficient retrieval and contextual usability 

🔹 Data Modeling & Architecture  • Develop and maintain robust data models including fact/dimension models, SCDs, wide tables, and CDC pipelines 

• Apply data versioning, incremental processing, partitioning, and clustering strategies to ensure consistency, reproducibility, and cost efficiency 

• Contribute to architectural decisions and trade-offs across storage, compute, and orchestration layers within the analytics platform 

• Help define and uphold data modeling standards, data contracts, and quality frameworks across teams 

🔹 Analytics, AI & ML Enablement  • Prepare high-quality datasets for ML model consumption, feature engineering workflows, and predictive/forecasting use cases 

• Contribute to a unified semantic layer that standardizes metrics, reusable definitions, and improves data access patterns 

• Partner with Data Science teams to operationalize feature pipelines and support model training, serving, and monitoring 

🔹 Quality, Governance & Reliability  • Implement data quality checks, contracts, and observability to ensure SLA/SLO adherence across pipelines 

• Work with metadata, lineage, and data discovery frameworks to improve transparency, governance, and trust in data 

• Drive improvements in pipeline reliability, monitoring, and incident response across the data ecosystem 

🔹 Collaboration & Technical Leadership  • Partner cross-functionally with Product, Analytics, and Data Science to translate ambiguous business problems into reusable data assets 

• Mentor junior engineers, review code/designs, and raise the bar for engineering quality and best practices 

• Communicate technical decisions, trade-offs, and system designs clearly to both technical and non-technical stakeholders