Senior Data Engineer (Data & Analytics)

Summit Consulting Services; Ernakulam; On-site; INR 1,000,000 - 1,800,000; Full time

Benefits offered by this job

Collaborative culture

Opportunity to influence architecture

Modern cloud-native tech stack

Job summary

Summit Consulting Services in Ernakulam seeks a Senior Data Engineer to design and optimize data pipelines and models. This deeply hands-on role emphasizes building reliable data infrastructure for analytics and services, requiring strong SQL and data transformation skills.

Join a collaborative team that values technical excellence and curiosity, while working on significant data challenges in a cloud-native environment.

Qualifications

• Strong experience in building robust data pipelines.
• Deep understanding of data modelling patterns.
• Experience in cloud platforms, preferably AWS.

Responsibilities

• Design and implement batch and streaming data pipelines.
• Develop ingestion frameworks for third-party data sources.
• Build and optimize ETL/ELT workflows.
• Implement data quality validation and monitoring.
• Collaborate with engineers to define data contracts.

Skills

Building production-grade data pipelines

Data modelling

Distributed data processing

SQL

Data quality frameworks

Debugging and performance optimization

Cloud-native environments

Data workflow orchestration tools

Tools

Airflow

Temporal

Job description

As a Senior Data Engineer, you will design, build, and optimize the data pipelines and models. You’ll focus on ingestion, transformation, modelling, quality, and data reliability — ensuring that downstream consumers (analytics, services, UI, AI) can depend on trusted, well-structured data. You’ll work closely with backend engineers, product teams, data operations, and architects to design data flows that are scalable, observable, and resilient. This role is deeply hands‑on and focused on data engineering excellence.

Key Responsibilities

• Design and implement robust batch and streaming data pipelines.
• Develop and maintain ingestion frameworks for broker feeds and third‑party data sources.
• Build and optimize data transformation workflows (ETL/ELT).
• Design scalable data models (warehouse/lakehouse patterns).
• Implement data quality validation, reconciliation, and monitoring.
• Contribute to metadata, lineage, and governance frameworks.
• Optimize performance and cost of data processing workflows.
• Improve reliability through observability, testing, and automated monitoring.
• Collaborate with backend and platform engineers to define data contracts and interfaces.
• Mentor engineers in best practices for data engineering and modelling.

Desired Qualifications

• Strong experience building production‑grade data pipelines at scale.
• Deep understanding of data modelling (warehouse, lake, semantic layers).
• Experience with distributed data processing and orchestration frameworks.
• Strong SQL and data transformation skills.
• Experience designing data quality frameworks and validation processes.
• Understanding of data architecture patterns (batch vs streaming, medallion architecture, etc.).
• Strong debugging and performance optimization skills.
• Experience working in cloud‑native data environments (preferably AWS, but other major cloud platforms are acceptable).
• Experience designing testing strategies for data pipelines.
• Experience with data workflow orchestration tools (e.g., Temporal, Airflow).
• Familiarity with financial or regulatory datasets – financial trading data experience will be a particular advantage.
• Exposure to semantic modelling, governance, or metadata management.

What We Offer

• A chance to work on genuinely interesting and important problems at scale.
• A collaborative, supportive culture that values curiosity, integrity, and technical excellence.
• The opportunity to influence architecture, tooling, and future direction.
• A team of smart, humble, mission‑driven people who care about our goals and about each other.
• A modern, cloud‑native tech stack and the freedom to bring your engineering voice to the table.