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Position Overview We are looking for a Data Engineer with hands-on experience in building scalable data pipelines and data engineering solutions on the Databricks Lakehouse Platform. The ideal candidate should have strong expertise in Python, PySpark, SQL, Databricks, AWS, and REST API integrations for data ingestion, managing large volumes of data, and data export ShyftLabs is a growing data product company that was founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation. Job Responsibilities: Design, develop, and maintain scalable ETL/ELT pipelines using Databricks, PySpark, and SQL. Integrate data from multiple sources, including databases, Amazon S3, files, and REST APIs Build data pipelines with Databricks Unity Catalog Implement business logic, data transformations, and dimensional data models Create, schedule, monitor, and optimize Databricks Jobs and Workflows Design and manage Delta Lake tables using Medallion Architecture (Bronze, Silver,Gold) Ensure data quality through validations, error handling, logging, and monitoring Optimize Spark workloads for performance, scalability, and reliability Collaborate with cross-functional teams to deliver production-ready data solutions Basic Qualification: Strong expertise in Python, PySpark, and Advanced SQL. Hands-on experience with the Databricks Lakehouse Platform Good understanding of Unity Catalog, Delta Lake, Databricks Workflows/Jobs, Clusters, Notebooks, Repos, and Medallion Architecture. Experience integrating with REST APIs for data ingestion and data export Strong knowledge of ETL/ELT development, batch processing, incremental loading, and data transformation. Experience with data modeling (Star Schema, Snowflake Schema, Fact & Dimension tables, SCD concepts). Understanding of data warehousing concepts and best practices Experience working with structured and semi-structured data (CSV, JSON, Parquet, Delta). Knowledge of partitioning, file optimization, Spark performance tuning, and query optimization. Experience with Git and CI/CD best practices Preferred Qualifications: 5+ years of experience in Data Engineering with 2+ years of hands-on Databricks experience. Experience with Auto Loader, Spark Declarative pipelines, Kafka, Airflow, or dbt is a plus Databricks certification is an added advantage We are proud to offer a competitive salary alongside a strong insurance package. We pride ourselves on the growth of our employees, offering extensive learning and development resources.
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.