Data Engineer Solution Architect

Experience:

Required Qualifications

• 8–12 years of experience in data engineering and architecture, including hands-on solution delivery.
• Deep expertise with Snowflake or Databricks, with strong working knowledge of tools like dbt, Matillion, SQL, and Python or PySpark.
• Experience designing and implementing data pipelines and orchestration using tools like Airflow, Control-M, or equivalent.
• Familiarity with cloud-native data engineering services (Such as AWS Glue, Redshift, Athena, GCP BigQuery, Dataflow, Pub/Sub, etc.) or similar.
• Strong understanding of data modelling, ELT/ETL design, and modern architecture frameworks (medallion, layered, or modular architectures).
• Experience integrating and troubleshooting APIs and real-time data ingestion technologies (Kafka, Kinesis, Pub/Sub, REST APIs).
• Familiarity with traditional ETL and data integration tools (Informatica, SSIS, Oracle Data Integrator, etc.).
• Excellent understanding of data governance, performance tuning, and DevOps for data (CI/CD, version control, monitoring).
• Strong communication, problem-solving, and stakeholder management skills.
  

Preferred Qualifications

• Certifications such as:
• Snowflake SnowPro, Databricks Certified Architect, AWS Data Analytics Specialty, or Google Professional Data Engineer.
• Prior consulting or client-facing experience.
• Exposure to AI/ML, data quality, or metadata management frameworks.
• Experience leading solution design across multi-cloud or hybrid environments.
  

Key Responsibilities

• Design and architect end-to-end data solutions using technologies like Snowflake, Databricks, dbt, Matillion, Python, Airflow, Control-M, and cloud-native services on AWS/Azure/GCP.
• Define and implement data ingestion, transformation, integration and orchestration frameworks for structured and semi-structured data.
• Architect data lakes and data warehouses with an emphasis on scalability, cost optimization, performance, and governance.
• Support real-time and API-based data integration scenarios; design solutions for streaming, micro-batch, and event-driven ingestion.
• Lead design and delivery of data visualization and reporting solutions using tools such as Power BI, Tableau, and Streamlit.
• Collaborate with business and technical stakeholders to define requirements, design architecture blueprints, and ensure alignment with business objectives.
• Establish and enforce engineering standards, frameworks, and reusable assets to improve delivery efficiency and solution quality.
• Mentor data engineers and help build internal capability on emerging technologies.
• Provide thought leadership around modern data platforms, AI/ML integration, and data modernization strategies.