Software Engineer – AWS-Snowflake Data Engineer

3 to 6 Years

India

Key Roles and Responsibilities

• Design, develop, and maintain scalable, secure, and high-performance data pipelines using AWS, Snowflake, and Python.
• Build end-to-end data engineering solutions, covering data ingestion, transformation, validation, storage, and consumption layers.
• Develop batch and real-time data integration frameworks to support business reporting, analytics, and AI/ML use cases.
• Create and optimize ELT/ETL processes to ensure efficient data movement across multiple source systems and cloud platforms.
• Implement robust data quality frameworks and proactively identify, analyze, and resolve data anomalies, inconsistencies, and performance issues.
• Perform root cause analysis for data-related incidents and implement preventive measures to improve platform reliability.
• Design and optimize Snowflake data models, schemas, warehouses, and workloads for performance and cost efficiency.
• Develop reusable Python components, automation scripts, and frameworks to improve engineering productivity and operational efficiency.
• Collaborate with business, analytics, and technology teams to understand data requirements and translate them into scalable technical solutions.
• Implement monitoring, alerting, logging, and observability mechanisms for data pipelines and platforms.
• Follow and promote engineering best practices, including code reviews, version control, CI/CD, automated testing, documentation, and secure development standards.
• Ensure adherence to data governance, security, compliance, and access control requirements.
• Continuously evaluate and adopt emerging technologies, tools, and best practices to improve the overall data platform ecosystem.

Skills & Competencies

Technical Skills

• Strong hands-on experience with AWS services such as S3, Lambda, Glue, Airflow, EventBridge, IAM, and related cloud-native services.
• Advanced proficiency in Snowflake, including procedures, data modeling, performance tuning, Snowpipe, Streams, Tasks, Dynamic Tables, and security frameworks.
• Strong programming skills in Python with experience developing scalable data processing frameworks and automation solutions.
• Expertise in building and maintaining end-to-end data pipelines in cloud environments.
• Strong knowledge of SQL and database design principles.
• Experience with data orchestration and workflow management tools such as Airflow or equivalent.
• Experience working with structured, semi-structured, and unstructured data.
• Knowledge of data quality, data lineage, metadata management, and monitoring practices.
• Familiarity with API integrations, data lake architectures, and modern analytics platforms.

Competencies

• Strong analytical and problem-solving skills with the ability to identify and resolve complex data issues.
• Proven ability to detect, investigate, and remediate data anomalies and quality concerns.
• Strong understanding of scalable architecture and data engineering design patterns.
• Commitment to coding standards, development best practices, and documentation.
• Excellent stakeholder communication and collaboration skills.
• Ability to work independently in a fast-paced, agile environment.
• Continuous learning mindset with a focus on innovation and operational excellence.
• Strong ownership, accountability, and attention to detail.

Other Pointers

1. Experience bracket for this requirement in terms of years – 3 to 6 years
2. Qualification criteria like education/certifications/skillsets –
   1. Education – B. Tech.
   2. Certifications – preferable - Snowflake or AWS certified data engineer
   3. Skillsets – AWS (S3, Lambda, Glue, Airflow, IAM, etc.), Snowflake, Python
3. Dedicated/Remote Support – Dedicated
4. Service window like

4. 1. time -9 AM To 6 PM
   2. days – Monday to Friday
   3. region like india/global – India, IST
   4. location of service – Kurla office

5. Deliverables like documentation/knowledge transfer – Yes.

5. 1. Documentation of all jobs/ design diagrams, etc.
   2. and KT needs to be completed before marking the project as completion.