Job Description & Details
This is a heavy-hitting Lead Data Architect role focused on driving a massive legacy migration from on-prem Hadoop to Snowflake for a major financial services player. You won't just be drawing architecture diagrams; you'll be actively shaping the strategy, wrestling with complex code conversion, and steering a distributed delivery team.
What You'll Actually Be Doing
Your day-to-day will be a mix of high-level strategy and deep technical problem-solving. You'll own the target-state Snowflake architecture—setting up data layers, ingestion frameworks, and transformation patterns—while figuring out how to untangle legacy Hive, Spark, and Python workloads. A big part of the gig involves leveraging AI-assisted code conversion tools, which means you need to set up rigorous validation, parallel runs, and quality controls rather than blindly trusting the output. You'll also be the bridge between the client's executive suite and your offshore delivery team in India, translating business needs into solid technical execution while helping pitch expansion opportunities to grow the account.
The Core Tech Stack
Snowflake is the non-negotiable heartbeat of this role, and you need at least four solid years of hands-on architecture experience with it, including Snowpark and modern cloud data pipelines. On the legacy side, deep expertise in Hadoop, Cloudera, Hive, and Apache Spark is critical because you'll be migrating away from them. Python and advanced SQL tie the whole execution layer together. Because this is financial services, you also need to know your way around data governance, automated CI/CD orchestration, data lineage, and strict regulatory compliance requirements.
Interview Expectations
Expect the hiring team to grill you on your exact strategy for data reconciliation and cutover planning during a high-stakes Hadoop-to-Snowflake migration; they are secretly testing whether you've actually survived complex production cutovers or just read about them in blog posts. They'll also likely present a scenario involving messy legacy Hive code or poorly optimized Spark jobs and ask how you would leverage AI-assisted tools to refactor them into Snowpark while keeping data quality airtight. They want to see how you balance speed with bulletproof engineering standards.
Application Advice
To get past the ATS, your resume needs to explicitly feature keywords like Hadoop-to-Snowflake migration, Snowpark, Apache Spark, data reconciliation, and client-facing architecture leadership. Don't just list technologies; write out bullet points that quantify the scale of the legacy data platforms you've modernised in the past. Highlight your experience in regulated industries like financial services and make sure your history of managing distributed, offshore delivery teams is front and center.