2+ Apache Jobs in Coimbatore | Apache Job openings in Coimbatore

Who are we ?

Searce means ‘a fine sieve’ & indicates ‘to refine, to analyze, to improve’. It signifies our way of working: To improve to the finest degree of excellence, ‘solving for better’ every time. Searcians are passionate improvers & solvers who love to question the status quo.

The primary purpose of all of us, at Searce, is driving intelligent, impactful & futuristic business outcomes using new-age technology. This purpose is driven passionately by HAPPIER people who aim to become better, everyday.

Tech Superpowers

End-to-End Ecosystem Thinker: You build modular, reusable data products across ingestion, transformation (ETL/ELT), and consumption layers. You ensure the entire data lifecycle is governed, scalable, and optimized for high-velocity delivery.

The MDS Architect. You reimagine business with the Modern Data Stack (MDS) to deliver Data Mesh implementations and real value. You treat every dataset as a measurable "Data Product with a clear focus on ROI and time-to-insight.

Distributed Compute & Scale Savant: You craft resilient architectures that survive petabyte scale volume and data skew without "breaking the bank. You prove your designs with cost-performance benchmarks, not just slideware.

Al-Ready Orchestrator: You engineer the bridge between structured data and Unstructured/Vector stores. By mastering pipelines for RAG models and GenAl, you turn raw data into the fuel for intelligent, automated workflows.

The Quality Craftsman (Builder @ Heart): You are an outcome-focused leader who lives in the code. From embedding GDPR/PII privacy-by-design to optimizing SQL, Python, and Spark daily, you ensure integrity is baked into every table

Experience & Relevance

Engineering Depth: 7-10 years of professional experience in end-to-end data product development. You have a portfolio that proves your ability to build complex, high-velocity pipelines for both Batch and Streaming workloads

Cloud-Native Fluency: Deep, hands-on experience designing and deploying scalable data solutions on at least one major cloud platform (AWS, GCP, or Azure). You are comfortable navigating the nuances of EMR, BigQuery, or Synapse at scale.

Al-Native Workflow: You don't just build for Al you build with Al. You must be proficient in using Al coding assistants (e.g.. GitHub Copilot) to accelerate your delivery and have a track record of building the data foundations required for Generative Al.

Architectural Portfolio: Evidence of leading 2-3 large-scale transformations-including platform migrations, data lakehouse builds, or real-time

analytics architectures.

Client-Facing Acumen: You have direct experience in a consultative, client-facing role. You can confidently translate a CEO's business vision into a Lead Engineer's technical specification without losing anything in translation.

The "Solver" Mindset: A track record of solving 'impossible data problems-whether it's fixing massive data skew, optimizing spiraling cloud costs, or architecting 99.9% available data services.

The candidate should have extensive experience in designing and developing scalable data pipelines and real-time data processing solutions. As a key member of the team, the Senior Data Engineer will play a critical role in building end-to-end data workflows, supporting machine learning model deployment, and driving MLOps practices in a fast-paced, agile environment. Strong expertise in Apache Kafka, Apache Flink, AWS SageMaker, and Terraform is essential. Additional experience with infrastructure automation and CI/CD for ML models is a significant advantage.

Key Responsibilities

1. Design, develop, and maintain high-performance ETL and real-time data pipelines using Apache Kafka and Apache Flink.
2. Build scalable and automated MLOps pipelines for training, validation, and deployment of models using AWS SageMaker and associated services.
3. Implement and manage Infrastructure as Code (IaC) using Terraform to provision and manage AWS environments.
4. Collaborate with data scientists, ML engineers, and DevOps teams to streamline model deployment workflows and ensure reliable production delivery.
5. Optimize data storage and retrieval strategies for large-scale structured and unstructured datasets.
6. Develop data transformation logic and integrate data from various internal and external sources into data lakes and warehouses.
7. Monitor, troubleshoot, and enhance performance of data systems in a cloud-native, fast-evolving production setup.
8. Ensure adherence to data governance, privacy, and security standards across all data handling activities.
9. Document data engineering solutions and workflows to facilitate cross-functional understanding and ongoing maintenance.