Senior Data Engineer, Data Management & BI

NBCUniversal•New York, NY

14h•$115,000 - $145,000•Remote

About The Position

The Senior Data Engineer, Data Mgmt and BI will design, build, and support scalable data solutions that enable trusted analytics, operational reporting, and data-driven decision making across the organization. This role will partner closely with product, analytics, engineering, and business stakeholders to translate ambiguous business needs into reliable technical solutions, while helping establish strong engineering standards across data pipelines, cloud platforms, data models, APIs, automation, and observability. The ideal candidate is a hands-on engineer who can work independently, mentor others, communicate clearly with technical and non-technical partners, and continuously improve how data is delivered, governed, tested, and supported in a fast-moving environment.

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 experience with 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 in 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 skills with the ability 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.

Nice To Haves

Experience working in media, advertising, streaming, analytics, or other data-intensive business environments.
Experience building pipelines that integrate files, APIs, event streams, relational systems, data lakes, and warehouse platforms.
Experience with orchestration and workflow tools such as Apache Airflow, AWS Glue, Amazon MWAA, or similar platforms.
Experience with infrastructure-as-code, cloud security, IAM, secrets management, and secure engineering practices.
Experience with BI and reporting tools such as Tableau, MicroStrategy, Looker, or similar tools.
Experience designing performant data models, curated datasets, APIs, or interfaces that support analytics, reporting, and operational decision making.
Familiarity with data governance, lineage, observability, data contracts, and production support practices.
Ability to operate independently in ambiguous situations, drive clarity, identify dependencies, and move work 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 across cross-functional teams, including 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.

Responsibilities

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 that support reliable analytics and operational workflows.
Develop reusable, maintainable code using modern data engineering practices, with an emphasis on performance, testability, observability, and long-term supportability.
Build and optimize batch, event-driven, and serverless data processing patterns using cloud-native services and distributed processing frameworks.
Contribute to architecture decisions across data lake, warehouse, orchestration, metadata, API, and application integration patterns.
Implement data quality checks, monitoring, alerting, and operational runbooks to ensure pipelines are accurate, reliable, and supportable in production.
Design and maintain CI/CD pipelines, automated testing, deployment workflows, and infrastructure-as-code patterns that improve delivery speed and reduce operational risk.
Partner with analytics, BI, product, and business teams to enable timely, trusted, 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 for simplification.
Participate in Agile delivery practices including sprint planning, backlog refinement, code reviews, release planning, incident response, and retrospectives.
Mentor engineers, promote engineering best practices, and help raise the quality bar for code, documentation, testing, and production readiness.
Create and maintain clear technical documentation, architecture diagrams, data lineage notes, implementation guides, and support materials as systems evolve.