Data Engineer Resume Examples

Junior Data Engineer — HCA Healthcare, a US hospital and healthcare services company

May 2025 – Present · 1 yr 6 mos

• Developed ETL workflows loading billing and operational data into a SQL Server reporting layer, writing SQL transformations, Python ingestion scripts, and validation queries for scheduled loads that feed 14 recurring Power BI datasets used by clinical operations and finance teams.
• Cut monthly job failures in a daily file-based ingestion process from 6 to 2 by adding row-count checks, null validation rules, and clearer error handling to scheduled jobs.
• Shortened average investigation time for reporting issues tied to source system changes from 90 to 35 minutes by building source-to-target mapping documentation and reusable SQL validation queries in Git-tracked workflows.
• Build and maintain scheduled batch pipelines in SQL and Python with guidance from senior engineers, and document source-to-target mappings, data quality checks, and workflow dependencies for reporting datasets across the hospital network.

Data Engineer — YETI, Outdoor products brand with ecommerce and wholesale operations

Jul 2023 – Present · 3 yrs 4 mos

• Built Snowflake ELT pipelines ingesting Shopify, Stripe, Google Analytics, and NetSuite data, delivering 26 production datasets used across marketing, finance, and supply chain reporting.
• Raised successful scheduled runs from 92% to 98.6% on daily ecommerce and marketing workflows by adding dbt tests, Airflow alerting, source freshness checks, and root-cause reviews.
• Cut median dataset delivery time for stakeholder reporting from 5 business days to 2 by publishing reusable dbt marts and aligning requirements with analysts before development.
• Lowered monthly Snowflake compute cost by 18% on high-volume customer and order models through reworked clustering, incremental model logic, and query tuning.
• Produced experiment-ready datasets for 11 A/B tests on checkout and retention initiatives, integrating event data with order and customer models and standardizing exposure and conversion logic.

Analytics Engineer — Favor Delivery, a Texas-based delivery platform and consumer marketplace

Jul 2021 – Jun 2023 · 1 yr 11 mos

• Developed curated data models for order funnel, customer retention, and courier operations reporting using reusable SQL transformations and dimensional models, supporting 18 Tableau dashboards used by product and operations teams.
• Improved data quality for daily KPI reporting by implementing validation checks and reconciling warehouse outputs against source-system extracts, cutting weekly metric discrepancies from 9 to 3.
• Automated ingestion of app event and support-platform extracts with scheduled Python workflows, replacing manual file handling and saving analysts 10 hours per week.
• Partnered with analysts on metric definitions and curated datasets for BI dashboards, maintaining Git-based development practices for model changes and releases.

Senior Data Engineer — Betterment, a digital investment and personal finance platform

Mar 2022 – Present · 4 yrs 8 mos

• Lead design of batch and streaming data architecture across AWS, Snowflake, Kafka, and Spark workflows, and set standards for data quality, schema management, lineage, and secure data handling in production pipelines
• Redesigned core transaction and account pipelines across batch and streaming workloads, combining Kafka event ingestion, Spark processing, and Snowflake serving models on AWS: reduced end-to-end data latency from 95 minutes to 18 minutes for 12 critical downstream datasets
• Cut P1 and P2 data incidents from 11 per quarter to 4 by implementing SLA tracking, schema checks, lineage documentation, and Datadog alerting across Airflow-managed workflows
• Lowered monthly Snowflake and S3 platform cost by $34,000 by reworking partition strategies, file sizing, incremental processing, and warehouse utilization patterns for finance and analytics workloads
• Led migration of legacy Python jobs into standardized orchestration and CI/CD patterns, raising on-time production deployments from 82% to 97% with reusable Airflow templates, GitHub Actions checks, and Terraform-managed infrastructure modules
• Mentored 5 data engineers through platform modernization, cutting median code review turnaround from 3 days to 1 day via design reviews, coding standards, and incident response runbooks

Data Engineer — Marqeta, a card issuing and payments platform for embedded finance

Feb 2018 – Feb 2022 · 4 yrs

• Built CDC-based ingestion for payment authorization and settlement data into Snowflake, designing Kafka-backed ingestion patterns and SQL models for incremental processing; improved data availability for reconciliation reporting from next-day to under 30 minutes.
• Strengthened pipeline reliability for finance-close datasets by adding Airflow dependency controls, retry logic, and production alerting with Datadog, raising SLA attainment from 88% to 99.2%.
• Cut average Spark job runtimes on large transaction history backfills from 4.5 hours to 2.1 hours by optimizing joins, partitioning strategy, and storage layout on AWS-based processing jobs.
• Developed Python and SQL pipelines for payments, ledger, and reconciliation data, and maintained Airflow orchestration, warehouse models, and operational monitoring for downstream analytics.

Business Intelligence Engineer — Green Dot Corporation, a banking and payments technology company

Jan 2015 – Jan 2018 · 3 yrs

• Delivered dimensional models for card transaction and customer support reporting, designing fact and dimension tables and standardizing business logic in SQL, which enabled 9 recurring executive and operational dashboards.
• Improved daily ETL stability for reporting workflows by refactoring legacy SQL procedures and adding validation checks to batch processes, cutting failed jobs from 14 per month to 5.

Data Analyst — Regions Bank, a regional consumer and commercial bank

Nov 2013 – Dec 2014 · 1 yr 1 mo

• Automated a recurring branch performance reporting process by replacing manual spreadsheet consolidation with scheduled SQL extracts and standardized report logic, saving 8 analyst hours per week.