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We are looking for a highly detail-oriented Data Engineer with solid expertise in MS SQL and Snowflake to support large-scale data transformation, reconciliation, validation, and data pipeline operations within a fast-paced FinTech/Lending environment. This role will primarily focus on developing and optimizing SQL-based data processes, transforming and validating financial datasets, maintaining data integrity across systems, and supporting operational reporting requirements. The ideal candidate should possess strong experience handling complex datasets, writing advanced SQL queries, troubleshooting data inconsistencies, and supporting end-to-end ETL/ELT workflows. This is a hands-on engineering role with strong emphasis on data processing, operational efficiency, and data quality rather than Data Science, Machine Learning, or advanced Analytics. Key Responsibilities Data Engineering & Transformation - Develop, optimize, and maintain complex MS SQL and Snowflake queries for data extraction, transformation, reconciliation, and validation processes. - Build and support ETL/ELT workflows for ingesting and transforming large-scale financial and operational datasets. - Perform data cleansing, standardization, transformation, and aggregation activities across multiple data sources. - Design and maintain efficient data processing pipelines to support business operations and reporting requirements. - Troubleshoot and resolve data discrepancies, transformation failures, and performance bottlenecks. Snowflake & Database Operations - Work extensively with Snowflake for data transformation, staging, data loading, and query optimization activities. - Develop and optimize stored procedures, views, CTEs, temporary tables, and complex joins in MS SQL Server and Snowflake. - Support data migration and synchronization processes between multiple systems and databases. - Monitor query performance and recommend improvements for efficient data processing and storage optimization. Data Validation & Reconciliation - Validate transformed datasets against source systems and business reports to ensure data accuracy and completeness. - Perform reconciliation activities on loan-level, transactional, operational, and financial datasets. - Investigate data mismatches, inconsistencies, and anomalies and coordinate resolution with internal teams. - Ensure adherence to internal data quality standards and governance processes. Process Improvement & Automation - Identify opportunities to automate repetitive manual data processes and improve operational efficiency. - Collaborate with Engineering, Operations, Credit, Finance, and Product teams to streamline data workflows. - Support continuous improvement initiatives related to data architecture, query performance, and transformation logic. Documentation & Collaboration - Maintain documentation for data flows, transformation logic, reconciliation procedures, and database objects. - Communicate technical issues, dependencies, and resolutions effectively to both technical and non-technical stakeholders. - Work closely with cross-functional teams to understand operational data requirements and implement scalable solutions. Requirements Required Experience & Skills - Bachelors degree in Computer Science, Information Systems, or related technical discipline. - 6+ years of experience in Data Engineering, Database Development, ETL Development, or Data Operations roles. - Strong hands-on expertise in writing advanced queries in MS SQL Server and Snowflake. - Extensive experience working with complex joins, stored procedures, functions, indexing, query optimization, CTEs, temporary tables, and performance tuning. - Strong experience in ETL/ELT processes, data transformation, and reconciliation workflows. - Experience working with large-scale structured financial or operational datasets. - Strong understanding of relational database concepts and data warehousing principles. - Experience handling data validation, data quality checks, and reconciliation processes. - Hands-on experience with Snowflake data loading, transformations, and performance optimization. - Exposure to orchestration or workflow tools such as Airflow or Dagster is an advantage. - Experience integrating and processing API-based datasets is preferred. - Prior experience working in FinTech, Lending, Financial Services, or similar domains is highly preferred. - Strong analytical thinking and problem-solving capabilities. - Excellent communication and stakeholder management skills. - Ability to work in a fast-paced, high-performance environment with strong attention to detail. Good to Have - Exposure to dbt-based transformation workflows - Understanding of AWS-based data environments - Experience with data reconciliation and operational reporting - Familiarity with financial, lending, or transactional datasets - Knowledge of production support .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.