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p> Job_Description\":\" Key Responsibilities Data Ingestion & Pipeline Engineering - Design and build reliable, scalable data pipelines ingesting from PostgreSQL, MySQL, MongoDB, Apache Kafka, and AWS SQS into Snowflake. - Implement CDC (Change Data Capture) patterns for near-real-time ingestion from transactional Aurora (PostgreSQL and MySQL) databases. - Build and maintain ELT pipelines using Python and dbt, ensuring data quality, lineage, and observability at every stage. - Handle schema evolution gracefully across heterogeneous source systems without breaking downstream consumers. Data Modelling & Warehouse Design - Design dimensional models, data vault structures, or medallion architecture layers (bronze/silver/gold) in Snowflake suited to SarvaGrams lending, collections, and field operations domains. - Own the Snowflake warehouse clustering keys, micro-partition strategy, materialized views, dynamic tables, and cost governance. - Define and enforce data modelling standards, naming conventions, and documentation practices across all datasets. Snowflake Administration - Manage Snowflake account administration user and role management, RBAC configuration, resource monitors, and virtual warehouse sizing. - Monitor and govern Snowflake credit consumption identify expensive queries, configure auto-suspend/auto-resume policies, and right-size virtual warehouses for different workload types. - Maintain Snowflake security posture network policies, data masking policies, row access policies, and column-level security for PII fields. - Manage Snowflake storage database, schema, and table lifecycle policies, time travel configuration, and Fail-safe awareness. - Drive Snowflake feature adoption (e.g. dynamic tables, Snowpark) and stay current with platform capabilities relevant to SarvaGrams data stack. Analytics Layer & Trino - Maintain and optimise the Trino query layer over Snowflake and other data stores, ensuring performant and cost-efficient analytical queries. - Collaborate with product and business teams to design semantic layers, aggregated marts, and self-serve datasets for reporting and dashboards. - Partner with the analytics/BI function to ensure Grafana, Metabase, or equivalent dashboards are backed by well-structured, tested data models. Data Quality & Observability - Implement data quality checks, anomaly detection, and freshness SLAs across all critical datasets. - Build alerting and monitoring for pipeline failures, schema drift, and data volume anomalies. - Maintain data cataloguing and lineage documentation so that any dataset consumed by product or business is traceable to its source. Cross-functional Collaboration - Work closely with backend engineers to understand source system schemas, event structures, and data contracts as new product features are built. - Translate analytical and reporting requirements from product managers and business stakeholders into well-scoped data engineering deliverables. - Participate in data governance discussions, especially around PII handling, DPDP compliance, and RBI audit data requirements. Requirements Requirements Must-Have - 3 to 5 years of hands-on data engineering experience. - SnowPro Core Certification - Demonstrable production experience with Snowflake not just familiarity. - Hands-on Snowflake administration experience RBAC, resource monitors, virtual warehouse management, and credit governance. - Working knowledge of Snowflake security features data masking policies, row access policies, and network policies. - Strong experience building ELT/ETL pipelines from relational (PostgreSQL, MySQL) and NoSQL (MongoDB) sources. - Hands-on experience consuming from event streaming systems Kafka and/or AWS SQS into a data warehouse. - Proficiency in Python for data pipeline development and orchestration. - Experience with Apache Airflow for pipeline orchestration and scheduling. - Strong understanding of data warehouse design star schema, dimensional modelling, or medallion/data vault approaches. - Experience with dbt (data build tool) for transformation, testing, and documentation. - Advanced SQL skills window functions, CTEs, query optimisation, execution plan analysis. - Ability to work directly with non-technical .
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.