Job Description

10Pearls is an award-winning end-to-end digital innovation company that helps businesses imagine and build the future. We are proud to announce that 10Pearls was named as winner of the Best Tech Work Culture Timmy Award in Washington DC by Tech in Motion, recognized on the Inc. 5000 Fastest-Growing Companies List, and was ranked the #1 Most Diverse Midsize Company in Greater Washington. We partner with businesses to help them transform, scale, and accelerate by adopting digital and exponential technologies. Our work has ranged from creating highly usable, secure digital experiences, mobile and software products, to helping businesses modernize through cloud adoption and development and the digitalization of their business processes. Our clientele is highly diverse, including Global 1000 enterprises, mid-market businesses, and high-growth start-ups. But those are just the facts. What makes us unique is that we have true heart and soul. We have a strong focus on a double bottom line and actively support and engage with the communities where we live and work to make the world a better place. In a nutshell, we believe in doing well, while doing good, and know how to balance the two.

Role

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.

Responsibilities

• 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