Senior Data Engineer

5 days ago

Kanpur, Uttar Pradesh, India appsierra Full time ₹ 8,00,000 - ₹ 24,00,000 per year

Data Engineering and Infrastructure: 6+ years of experience in a Data Engineering role, designing, building, and managing

scalable and reliable data systems.

  • Proficient with big data and stream-processing technologies such as Spark and Kafka.
  • Hands-on experience with cloud platforms, particularly AWS services like EC2 and RDS.
  • Skilled in building and orchestrating data pipelines using tools like Airflow.
  • Experience with Databricks for scalable data processing and advanced analytics.
  • Strong knowledge of SQLMesh for modern data workflow management.
  • Extensive experience integrating and working with external data sources via REST APIs,

GraphQL endpoints, and SFTP servers.

  • Strong communication skills and leadership capabilities are required.

Databases and Data Management: Expertise with relational and NoSQL databases, including Postgres and MongoDB.

  • Solid understanding of data modeling, data governance, and data security best

practices.

Programming and Development: Proficient in Python for data engineering, automation, and workflow scripting.

  • Familiarity with software engineering best practices, including version control, testing,

and CI/CD pipelines for data workflows.

  • Experience with JavaScript and TypeScript is a plus.

Analytics, Visualization, and BI: Skilled in implementing and supporting self-service BI tools to enable business teams

with accessible, actionable insights.

  • Experience with Streamlit for building interactive data visualizations is a plus.

Blockchain and Financial Data Expertise: Knowledge of blockchain technology and the cryptocurrency ecosystem is a nice-to-

have, with a strong interest in staying up to date with emerging trends.

  • Experience working with financial datasets and financial engineering concepts is

considered a strong advantage.

Our Stack

We work with a modern and evolving technology stack, including but not limited to:

  • Cloud Infrastructure: AWS for cloud services and infrastructure management
  • Databases: PostgreSQL for relational data, MongoDB for non-relational (NoSQL) data,

and Redis for caching and real-time data management

  • Backend: NestJS , TypeScript) and Python for building scalable backend

services

  • Frontend: React for web applications, Streamlit for building interactive data

visualizations

  • Data Engineering: Airflow and SQLMesh for data pipeline orchestration and modern

workflow management

  • Big Data & Processing: Databricks and Kafka for scalable data processing, analytics,

and streaming

  • Integrations & APIs: Extensive use of REST APIs, GraphQL, SFTP, and Slack

integrations to enable seamless data exchange and operational workflows

  • Messaging & Event Streaming: Kafka for real-time data pipelines and event-driven

architectures

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