🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
We are looking for a highly skilled Senior Data Engineer with strong experience in modern data engineering and cloud data platforms. The ideal candidate will have hands-on experience with Snowflake or Databricks, along with a strong understanding of data engineering, ETL/ELT processes, data modelling, data warehousing, and cloud platforms. Hands-on experience with at least one of Snowflake or Databricks is mandatory, while working knowledge of the other platform is good to have. The candidate will be responsible for designing, developing, optimizing, and maintaining scalable data pipelines and data solutions. The role will involve working closely with data engineers, data scientists, analysts, and business teams to deliver reliable and high-quality data solutions. Apply now Key Responsibilities Data Architecture & Development: Design, develop, and optimize scalable, secure, and high-performance data solutions using Snowflake and/or Databricks. ETL/ELT Pipeline Engineering: Build and maintain robust data pipelines with appropriate orchestration, monitoring, retries, logging, and error handling. Data Modelling & Transformation: Develop scalable data models and transformations following data warehousing and lakehouse best practices. Snowflake / Databricks Development: Develop and maintain data solutions using Snowflake or Databricks and optimize data processing and query performance. Advanced Data Engineering & Optimization: Develop and optimize complex SQL and PySpark transformations for large-scale batch and incremental processing. Implement CDC, merge/upsert patterns, partitioning, and schema evolution while performing hands-on tuning of Snowflake queries, Spark jobs, and Delta Lake workloads. Build pipeline observability through execution logging, source-to-target reconciliation, data completeness checks, error diagnostics, and automated failure handling. Data Quality: Implement data validation, automated testing, monitoring, and quality frameworks to ensure data integrity and reliability. Performance Optimization: Monitor pipeline and job performance, troubleshoot issues, and optimize queries and data processing workloads. Cloud Data Engineering: Develop and manage data pipelines and solutions on AWS, Azure, or GCP. Collaboration: Work with data analysts, data scientists, and business stakeholders to translate requirements into technical specifications and deliverables. Technical Contribution: Participate in code reviews, technical discussions, design reviews, and establish data engineering best practices. Innovation & Research: Stay updated on advancements in Snowflake, Databricks, cloud data platforms, AI/ML, and modern data engineering practices. Qualifications Bachelors or Masters degree in Computer Science, Information Technology, Data Engineering, or a related field.5+ years of experience in data engineering, with hands-on experience in at least one of Snowflake or Databricks.Strong understanding of:Data modellingData warehousingETL/ELT processesData Lake / Lakehouse architecturesData quality and validationSnowflake: Hands-on experience with data modelling, query/performance optimization, access control, streams, tasks, external tables, or equivalent capabilities.Databricks: Hands-on experience with data engineering, Spark, Delta Lake/Lakehouse concepts, and data pipeline development.Snowflake or Databricks one must be hands-on; knowledge of the other is good to have. Proficiency in SQL and Python. Spark experience is expected for candidates with Databricks exposure.Experience building data pipelines on cloud platforms such as AWS, Azure, or GCP.Experience with orchestration tools such as Apache Airflow, Azure Data Factory, AWS Glue, or equivalent.Familiarity with dbt and modern ELT practices is good to have. Familiarity with Git and CI/CD practices.Strong analytical, problem-solving, and communication skills.Ability to work independently as well as collaboratively in a team environment. Preferred Skills Hands-on experience with both Snowflake and Databricks.Experience with Apache Airflow and dbt.Experience with streaming data pipelines such as Kafka, Kinesis, or Pub/Sub.Exposure to Generative AI, ML, or advanced analytics solutions.Familiarity with BI/analytics tools such as Power BI, Tableau, or Looker.Knowledge of data governance, security, and compliance best practices.Familiarity with Terraform or other infrastructure-as-code tools.Exposure to multiple cloud platforms such as AWS, Azure, or GCP. Perks Flexible Timings5 Days WorkingHealthy EnvironmentCelebrationLearn and GrowBuild the CommunityMedical Insurance Benefit .
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.