This is a remote position and reports to the VP of Data and Analytics, the Lead Data Engineer will be charged with implementing data infrastructure that is core to the Data and Analytics team mission. This infrastructure should be built using modern-day data tools and platforms and will support the technology strategy and services that enable data-powered work and maximize Audubons mission impact. Data science, analytics, geographic mapping, and business intelligence are essential to how Audubon does its work across business teams including conservation, science, advocacy, marketing, fundraising, and finance. The Data and Analytics team plays a key role in empowering that work and needs an engineer who will be responsible for building and maintaining the infrastructure that will enable this mission.
They will work closely with the VP of Data and Analytics to design and implement a new strategic data platform for the National Audubon Society that will support projects such as:
- Migratory Bird Initiative, a multi-year project to track and aggregate varied data sources about migratory bird species, where they go, and the threats they face
- Integrate data from multiple sources (e.g., email, website interactions, CRM and volunteer activity, voter files, demographic data to support inclusion and equity metrics) into a data warehouse and building optimized views and workflows for analysis, reporting, and dashboard visualization
- Identify, evaluate, and implement infrastructure solutions for unified business intelligence, data warehousing, scaling and parallel processing, and machine learning
- Proactive data analysis to identify issues and implement a plan to improve the quality and integrity of operational data, particularly constituent data across data-heavy systems like Salesforce and EveryAction
- Advise on the design, build, and maintenance of structured databases to track curated data such as bird species taxonomies, bird-friendly native plants by geolocation, and important bird areas, along with product and engineering staff
- Convert raster data of predictive bird species ranges based on climate change models to vector features to enable enterprise GIS analysis and further conservation research
Qualifications and Experience
- Contribute to the design of a new strategic data infrastructure to support the National Audubon Society mission.
- Take ownership of building and maintaining the new data infrastructure, including data platforms, data pipelines, and analytical and business intelligence tools.
- Develop run books and documentation to detail all data infrastructure design and operations, including backup and recovery.
- Collaborate with the Enterprise Geographic Information Systems (GIS) team to leverage enterprise GIS and mapping technologies as part of the overall data architecture.
- Advise the VP of Data and Analytics, as well as others throughout the organization, on data-related tools and platforms, data best practices for integrity, testing, and modeling, and analytical approaches.
- Collaborate with the peer Engineering team to ensure data quality and to advise on the design and implementation of system integrations and API services.
- Work with the VP of Data and Analytics and the Director of IT to support data governance initiatives and to ensure the reliability and security of data services and infrastructure.
- 7 years of experience as a Data Engineer, or in similar roles, with increasing levels of responsibility.
- Degree in Computer Science, Statistics, Data Science, Mathematics, or related quantitative field strongly preferred.
- Significant hands-on experience building and working with data lakes, warehouses, marts, pipelines, and providing reporting or analytical services.
- Fluency in Linux-based operating systems.
- Experience with RDBM Systems and deep SQL experience.
- Experience with cloud data warehouse solutions like Redshift, Snowflake, Azure Data Warehouse, and/or BigQuery.
- Experience with cloud infrastructure (e.g., AWS EC2, S3, Lambda or equivalent services from Azure or Google).
- Experience with Linux scripting, Python, Java, Scala, .NET, R or other programming languages proven to be robust and widely used for ETL/ELT and data analytics.
- Experience with business intelligence tools (e.g., Tableau, Looker, PowerBI).
- Experience with or strong understanding of ETL/ELT and workflow tools like AWS Glue and Apache Airflow.
- Experience with or strong understanding of big data architectures and data modeling to efficiently process large volumes of data, including solutions like Spark, Hadoop, EMR, Kafka, etc.
- Experience supporting data science notebook environments such as Jupyter Hub or R Studio preferred.
- Familiarity with machine learning/data science platforms and services like Sagemaker, Domino, TensorFlow, etc. a plus.
- Demonstrated ability to communicate technical information to non-technical audiences.
- Self-starter who can work as part of a virtual team and remain motivated in a dynamic environment.
- Genuine passion for conservation and the mission of the National Audubon Society.
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