Senior Software Developer at Tecsys Inc.

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

13 Oct, 26

Salary

0.0

Posted On

15 Jul, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, Python, Databricks, Apache Spark, ETL/ELT, Data Modeling, Airflow, Cloud Platforms, Data Lakehouse, Kafka, CI/CD, Docker, Kubernetes, Data Governance, Feature Engineering, Distributed Processing

Industry

Software Development

Description

About us Tecsys 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 Required Skills & Experience Technical Skills Advanced proficiency in SQL and Python, with strong focus on query optimization, cost efficiency, and large-scale data processing Hands-on experience with Databricks, Apache Spark, and distributed processing frameworks Strong experience building ETL/ELT pipelines and workflow orchestration (Airflow, Dagster, or similar) Solid understanding of Data Lake / Lakehouse architectures, storage formats (Parquet, Delta/Iceberg), partitioning, clustering, and performance tuning Deep expertise in data modeling: fact/dimension models, SCDs, wide tables, CDC, data versioning, and incremental processing Experience with event-driven and streaming architectures (Kafka, Pub/Sub, Kinesis) applied pragmatically alongside batch where needed Cloud & DevOps Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) and cloud data warehouses (Snowflake, BigQuery, Redshift) Familiarity with Docker, Kubernetes, CI/CD pipelines, and infrastructure-as-code for data systems Reliability, Observability & Governance Experience building production-grade data systems with strong data quality, observability, and SLA/SLO practices Familiarity with metadata management, data lineage, and data discovery tools (e.g., DataHub, OpenMetadata, Amundsen) AI / ML & Analytics (Preferred) Experience enabling AI/ML use cases from a data perspective — preparing datasets, feature stores, embeddings, or semantic/metric layers (dbt, Cube, LookML) Exposure to BI tools (Power BI, Tableau, Looker) and KPI modeling for business stakeholders Domain Knowledge (Preferred) Experience in Supply Chain, Logistics, or Healthcare supply chain analytics is a strong plus Familiarity with domains such as Search, Recommendations, or Marketing analytics Soft Skills Proven ability to translate ambiguous business requirements into scalable data models and systems Ownership mindset — drives features end-to-end with strong communication and collaboration skills Qualifications Bachelor's or Master's in Computer Science, Engineering, or related field — or equivalent practical experience 6–8+ years of experience in Data Engineering, building and operating production-grade data platforms at scale Demonstrated experience delivering data solutions across analytics, ML, or product-facing domains This role will require you to be based in Bengaluru. At Tecsys, we value creativity, innovation, and teamwork. Our employees enjoy a supportive work environment, competitive compensation packages, and opportunities for career growth and advancement. Tecsys is an equal opportunity employer. *** A Note on Our Hiring Process: We do not use AI to automatically screen or reject candidates. However, we do use specific screening questions to prioritize the most relevant applications for human review. At Tecsys, we welcome the thoughtful use of AI tools to help you prepare your application, for example, to improve clarity, organize your resume, or practice interview responses. However, we ask that all information you provide reflects your real experience, and that any assessments or written submissions represent your own work and thinking. During interviews, we expect candidates to engage without the use of AI tools, scripts, or real-time assistance. Authentic, direct conversation helps us get to know how you think, collaborate, and communicate. AI can support your preparation, but it shouldn’t speak or act on your behalf. We genuinely want to meet you.

Responsibilities

Design and build scalable ETL/ELT pipelines and data models on Databricks and Spark to support AI-consumable datasets. Collaborate with cross-functional teams to operationalize feature pipelines and ensure data quality and observability.