Data Engineering Architect
First seen by Alion on Aug 11, 2026.
Experience :
- 6+ years of experience in data engineering, data warehousing, or big data platforms.
- Proven experience in AWS cloud ecosystem (S3, Glue, EMR, Lambda, Redshift, Athena).
- Strong hands-on expertise with Apache Spark (Core, SQL, Streaming) and PySpark.
- Practical knowledge of Databricks for collaborative data engineering and ML workflows.
- Proficiency in Snowflake for data warehousing and analytics use cases.
- Strong programming skills in Python and SQL.
- Expertise in data modeling, ETL/ELT pipeline design, and data warehousing concepts.
- Experience with distributed and real-time processing frameworks like Flink.
- Exposure to at least one BI tool (Tableau, Power BI, or QuickSight).
- Domain understanding in BFSI/ Fintech (preferred).
- Preferred experience in working with either Snowflake or Databricks.
- Prior experience in designing and implementing at least 2+ large-scale production data migration or modernization projects.
- Industry knowledge in Retail, Logistics, FSI, or Manufacturing would be an added advantage.
Roles & Responsibilities :
- Design and architect scalable, secure, and performant big data solutions using AWS, Snowflake, or Databricks.
- Strong understanding of performance tuning, cost optimization, and pipeline monitoring for reliable production workloads.
- Partner with business leaders, product managers, and clients to translate business objectives into technical data solutions.
- Establish and enforce coding standards, architecture guidelines, and design patterns across projects.
- Lead and mentor a team of data engineers and analysts, promoting best practices in data design and engineering.
- Conduct code and architecture reviews, ensuring high-quality deliverables and adherence to technical standards.
- Collaborate with cross-functional teams to ensure seamless data delivery and alignment with business goals.
Key Skills :
- Technologies : AWS, Snowflake, Databricks, Spark, Python, SQL, Airflow, Kafka.
- Frameworks : Spark Core, Spark SQL, Spark Streaming/Flink.
- Soft Skills : Leadership, stakeholder management, Team Management, communication, problem-solving, decision-making.
Skills
Data Engineering, Data Architect, Data Warehousing, Azure Databricks, Snowflake DB, AWS, PySpark, Python, SQL, ETL