Senior Data Engineer with 10+ years of experience designing, orchestrating, and supporting data pipelines and analytical data models. Proven track record transforming complex, fragmented data into scalable, reusable datasets using modern ETL tools across multi-cloud environments.
I'm Janesyn Abit, a Data Engineering and Governance Associate Manager with a decade-long career building the backbone of modern data platforms. I specialise in designing end-to-end data pipelines that are resilient, observable, and built to scale.
My work sits at the intersection of engineering and governance — not just moving data, but ensuring it is trusted, documented, and reusable across the organisation. I've delivered solutions for enterprise clients at both Accenture and Telstra, operating across AWS, Azure, and GCP environments.
I'm passionate about data quality, lineage, and the invisible engineering that makes analytics reliable for everyone downstream.
Designed and maintained enterprise-grade data pipelines for large-scale clients, delivering end-to-end ETL solutions across cloud platforms. Collaborated with business stakeholders to translate complex requirements into scalable data models and governance frameworks. Led data quality initiatives and contributed to the implementation of data cataloguing and lineage tooling.
Supported and extended Telstra's core data platform, building reliable pipelines that served analytical and operational consumers. Worked across data ingestion, transformation, and serving layers — ensuring SLA compliance, pipeline observability, and data integrity at enterprise scale. Contributed to data governance policies and metadata management practices.
NOAA Weather data pipeline built end-to-end on free-tier accounts.
End-to-end data pipeline ingesting live weather forecast data from the NOAA public API across four US cities. Built on Databricks Serverless Compute using managed Delta tables throughout — Bronze raw ingest, Silver parse and cleanse, Gold daily aggregations, and a SQL serving layer with a live dashboard. Deliberately avoids legacy DBFS patterns in favour of Unity Catalog managed tables.
End-to-end data pipeline in Snowflake processing NOAA weather forecast data across four US cities. Built a RAW → CLEAN → SERVING medallion architecture using internal stages, VARIANT JSON parsing, and typed transformations. Includes a published Snowsight dashboard with temperature trends, rain risk, and detail views. Demonstrates cross-platform data movement from Databricks to Snowflake.
Same NOAA pipeline rebuilt on each major cloud platform's native services.
Cloud-native pipeline using Cloud Storage, Dataflow, BigQuery, and Looker Studio.
Cloud-native pipeline using Data Factory, ADLS Gen2, Synapse Analytics, and Power BI.
Cloud-native pipeline using S3, Glue, Redshift, and QuickSight.
I'm open to senior data engineering roles, consulting engagements, and collaborative projects — particularly where data governance, pipeline reliability, or cloud architecture are at the core.
Reach out via email or connect with me on LinkedIn and GitHub.