Senior Data Engineer, Data Management & BI

Accountabilities:: Design, build, test, deploy, and maintain scalable data pipelines that ingest, transform, validate, and publish structured and semi-structured data from internal and external sources. Translate business and product requirements into technical designs, data models, integration patterns, and execution plans supporting analytics and operational workflows. Develop reusable and maintainable code using modern data engineering practices, with a strong focus on performance, testability, observability, reliability, and long-term supportability. Build and optimize batch, event-driven, and serverless data-processing solutions using cloud-native services and distributed processing frameworks. Contribute to architecture decisions covering data lakes, data warehouses, orchestration, metadata, APIs, and application integration patterns. Implement data quality controls, monitoring, alerting, and operational runbooks to ensure production pipelines remain accurate, reliable, and supportable. Design and maintain CI/CD pipelines, automated testing, deployment workflows, and infrastructure-as-code practices that accelerate delivery while reducing operational risk. Partner with analytics, BI, product, and business teams to deliver trusted, timely, and well-documented data products and reporting solutions. Evaluate source systems, APIs, files, data contracts, and transformation logic to identify risks, dependencies, data gaps, and opportunities to simplify data flows. Participate in Agile delivery activities including sprint planning, backlog refinement, code reviews, release planning, incident response, and retrospectives. Mentor engineers and promote strong engineering practices across code quality, documentation, testing, production readiness, and operational support. Create and maintain technical documentation, architecture diagrams, data lineage information, implementation guides, and support materials as platforms and systems evolve. Requirements Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Analytics, or a related field, or equivalent practical experience. 5+ years of hands-on data engineering experience designing, building, testing, deploying, and supporting production data pipelines. Strong knowledge of data modeling, data architecture, ETL/ELT patterns, metadata, data quality, and data warehouse or data lake methodologies. Hands-on experience with cloud data engineering services such as AWS S3, Lambda, Athena, EMR or EMR Serverless, Glue, Step Functions, SNS, SQS, or comparable technologies. Strong programming experience with Scala, Java, SQL, or similar languages used for data processing, automation, and integration. Experience with distributed processing and modern data platforms such as Spark, Databricks, Snowflake, Apache Iceberg, Hive, Redshift, Postgres, SingleStore, or comparable technologies. Experience designing and maintaining CI/CD pipelines, source control workflows, automated testing, and deployment practices using GitHub Actions or similar tools. Strong understanding of Agile delivery, DevOps practices, incident response, production support, monitoring, and performance tuning. Strong analytical and problem-solving abilities, with the capacity to troubleshoot complex data, application, and platform issues. Excellent written and verbal communication skills, including the ability to explain technical concepts clearly to engineers, product owners, business stakeholders, and leadership. Experience in media, advertising, streaming, analytics, or other data-intensive environments is preferred. Experience integrating files, APIs, event streams, relational systems, data lakes, and data warehouse platforms is desirable. Familiarity with orchestration and workflow technologies such as Apache Airflow, AWS Glue, Amazon MWAA, or similar platforms is preferred. Experience with Infrastructure as Code, cloud security, IAM, secrets management, and secure engineering practices is a plus. Experience with BI and reporting platforms such as Tableau, MicroStrategy, Looker, or similar technologies is desirable. Experience designing performant data models, curated datasets, APIs, or interfaces that support analytics, reporting, and operational decision-making is preferred. Familiarity with data governance, lineage, observability, data contracts, and production support practices is beneficial. Ability to operate independently in ambiguous situations, create clarity, identify dependencies, and drive initiatives from concept through delivery. Strong ownership mindset with the ability to balance delivery speed, engineering quality, stakeholder communication, and operational readiness. Ability to collaborate effectively with engineering, product, analytics, operations, vendors, and business stakeholders. Curiosity about emerging data engineering, automation, and AI-assisted development practices, with a practical focus on improving engineering productivity and solution quality. Benefits $115,000–$145,000 annual salary . Fully remote position within the United States. Medical, dental, and vision insurance. 401(k) benefits. Paid leave. Tuition reimbursement. Additional employee discounts and perks. Opportunity to work with modern cloud data platforms, distributed processing technologies, BI solutions, and data architecture patterns. Broad cross-functional exposure across engineering, product, analytics, operations, and business teams. Opportunities to mentor other engineers and influence engineering standards and data practices. Ongoing opportunities to work with automation and AI-assisted development technologies. Applications accepted on an ongoing basis.