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We are hiring for a Senior Data Engineer at 10Pearls responsible for designing, building, and optimizing scalable data pipelines and cloud-based data platforms. The role involves working with modern ETL/ELT tools, data warehouses, real-time data systems, and cloud storage solutions to support analytics and business intelligence initiatives. The engineer collaborates with cross-functional teams to develop reliable, high-performance data solutions using technologies such as Airflow, Snowflake, SQL Server, Python, dbt, and Azure services, while ensuring data quality, automation, and efficient CI/CD practices across production environments.
Job Responsibility
Analyze large, complex datasets to extract key insights that inform strategic decision-making
Develop and maintain data models, dashboards, and reports to support business objectives
Apply statistical techniques and predictive modeling to drive business forecasts and performance analysis
Collaborate with data consultant teams to build scalable, reliable data pipelines and optimize data flows
Ensure data quality and integrity through best practices in data governance and validation processes
Partner with cross-functional teams (engineering, product, marketing) to understand data requirements and deliver customized insights
Present findings and actionable recommendations to senior leadership and non-technical stakeholders
Lead, mentor, and provide guidance to junior data analysts and team members
Manage multiple data projects, prioritize deliverables, and meet tight deadlines with high quality output
Implement CI/CD pipelines and version control for data workflows to ensure seamless deployment and collaboration
Stay up to date with the latest trends and technologies in data analytics and propose strategic improvements for ongoing data initiatives
Design, develop, and optimize interactive dashboards using visualization tools, ensuring clarity and usability for business users
Understand client needs and provide tailored, strategic solutions that align with business objectives
Build strong relationships by clearly communicating technical concepts and managing expectations
Actively participate in recruiting top technical talent for the team
Requirements
5+ years of professional data engineering experience, including ownership of production pipelines that other teams depended on
ETL / ELT: deep experience with Airflow (DAG design, sensors, retries, backfills) and Airbyte (or equivalents like Fivetran, Meltano, or custom connectors)
Data warehousing: strong hands-on experience with Snowflake and/or BigQuery, including performance tuning, partitioning/clustering, and cost optimization
Transactional databases: solid SQL Server experience covering schema design, indexing, query tuning, and CDC patterns
Real-time data: production experience with Firebase / Firestore or comparable real-time stores, and at least one streaming or message-bus technology (Kafka, Azure Service Bus, Pub/Sub, Kinesis)
Programming: advanced SQL plus strong Python for data engineering
comfortable building reusable, tested, well-documented code
Modeling: fluency with dimensional and modern warehouse modeling, and with transformation frameworks such as dbt
Cloud storage: hands-on experience with Azure Storage (Blob/ADLS)
comfort with AWS S3 or GCS is a plus
Experience working with CI/CD for data pipelines (GitLab CI or equivalent), including environment promotion and automated testing
Strong written and verbal communication in English
Nice to have
Experience supporting ML use cases: feature stores, training/serving skew, point-in-time correctness, and dataset versioning
Familiarity with Kubernetes-based data platforms and running Airflow on AKS or equivalent
Experience with data quality frameworks such as Great Expectations, Soda, or dbt tests at scale
Domain experience in supply chain, logistics, e-commerce, or warehousing
Familiarity with Azure AI Foundry data integration patterns