We are looking for a Data Engineer with 3–6 years of experience who thrives on solving complex data problems, building scalable data systems, and enabling high-quality analytics across the organization.
This role will play a critical part in onboarding newly acquired brands, integrating diverse data ecosystems, and building reliable, scalable data infrastructure that powers decision-making across business, product, and operations.
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Design, build, and maintain scalable data pipelines and ETL/ELT workflows for batch and near real-time processing
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Develop and optimize data models, schemas, and warehouse structures for analytics, reporting, and downstream applications
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Build robust data ingestion frameworks integrating multiple sources including databases, APIs, SaaS tools, and third-party platforms
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Implement data transformation, validation, and quality checks for partially structured and unstructured datasets
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Perform root-cause analysis on data issues, ensuring reliability, accuracy, and observability of pipelines
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Build and manage data infrastructure on cloud platforms, optimizing performance and cost
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Collaborate closely with product managers, analysts, data scientists, and engineering teams to deliver business-ready datasets
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Enable secure data access, governance, and documentation across systems
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Contribute to standardized onboarding and migration frameworks for integrating newly acquired brands into Mensa’s data platfor
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Bachelor’s degree in Computer Science, Engineering, or related field
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3–6 years of hands-on experience in data engineering or backend data systems
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Strong proficiency in SQL and relational databases (PostgreSQL, MySQL, etc.)
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Strong programming skills in Python (preferred), with exposure to Java or Scala
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Hands-on experience building ETL/ELT pipelines and data transformation frameworks
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Experience with cloud data platforms and warehousing such as Amazon Redshift, BigQuery, or Snowflake
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Practical experience with AWS ecosystem, especially:
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AWS Glue / Glue Jobs
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Amazon S3
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Redshift
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Lambda / Step Functions (nice to have)
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Experience working with big data processing frameworks such as Apache Spark
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Strong understanding of data modeling, partitioning, indexing, and performance tuning
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Familiarity with Git-based version control and CI/CD workflow
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Strong analytical mindset with attention to data accuracy, structure, and scalability
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Experience with workflow orchestration tools like Airflow, Dagster, or Prefect
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Exposure to streaming and real-time data systems such as Kafka, Kinesis, or Pub/Sub
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Familiarity with data lake architectures and lakehouse patterns
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Understanding of analytics, BI, or machine learning data requirement
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Experience working in a startup, high-growth, or acquisition-driven environment
