ATS score guide for Data Engineer at Meta (Hack/PHP, Python, C++, React) — move fast. Skills, keywords, and what it takes to pass Meta's ATS screening for Data Engineer roles. Use this guide to understand what Meta's ATS looks for — and check your own resume with our free AI-powered analyzer.
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Structure your resume around the data systems you have built and the decisions those systems enabled. Each bullet should follow a pattern: what you built, at what scale, and what outcome it drove. Specify the volume of data you worked with (rows per day, warehouse size, event throughput) because scale is a key differentiator for Meta roles. List your technical stack explicitly: SQL dialects, Spark, Airflow, Kafka, dbt, Presto, Python, Java. If you have experience with data governance, privacy-compliant data handling (particularly relevant given Meta's regulatory scrutiny), or data quality frameworks, include it. Remove vague phrases like helped manage data infrastructure and replace them with concrete achievements. If you transitioned from analytics or software engineering into data engineering, frame the move as expanding your scope rather than changing careers — Meta values T-shaped engineers who understand the full data lifecycle.
Data engineers at Meta design, build, and maintain some of the world's largest data platforms, supporting analytics, machine learning, and product development across Facebook, Instagram, WhatsApp, and Reality Labs. The data infrastructure processes petabytes of data daily using a combination of internal tools and open-source technologies Meta has built or contributed to, including Presto (now Trino), Apache Spark, Hive, and Scuba. Data engineers partner with data scientists, ML engineers, and product managers to ensure clean, reliable, and timely data pipelines. The role spans batch and real-time processing, data modeling, warehouse design, and pipeline orchestration. Teams are organized by product area, and there are also centralized data infrastructure teams building the platforms everyone else relies on. Compensation ranges from $147,000 to $208,000 in base salary, with significant equity and bonus on top.
These are the skills most commonly required in Meta's Data Engineer job descriptions. Make sure they appear verbatim in your resume to pass ATS screening.
Meta's data engineering bar emphasizes SQL fluency, pipeline design, and data modeling at scale. Hiring managers want to see that you can design efficient data models for analytical workloads, build fault-tolerant ETL pipelines, and reason about data quality at the petabyte level. Experience with distributed computing frameworks like Spark, Presto, or Hive is strongly preferred. Resumes that mention experience with real-time streaming (Kafka, Flink) and workflow orchestration (Airflow, Prefect) stand out. The most common reason data engineering resumes get rejected is that they focus on tooling without showing impact — saying you built a pipeline is not enough. Meta wants to know how your pipeline reduced data latency, improved model training times, or enabled a product decision that moved a metric. If you have worked at scale (billions of records, petabyte warehouses), make that scale explicit in your resume.
These are the most frequent reasons Data Engineer resumes fail to pass Meta's ATS or get filtered during recruiter review.
Listing 'built pipelines' without data volumes, sources, or reliability metrics
Not differentiating from data science — emphasize infrastructure and reliability
Missing data quality or testing experience (Great Expectations, dbt tests)
Not featuring Hack/PHP, Python, C++ prominently — Meta Data Engineer roles rely heavily on this stack
Meta values impact over process — lead every bullet with measurable impact (users affected, revenue generated, latency reduced). Ignoring this is a common reason Meta resumes get filtered
The data engineering interview loop typically includes a recruiter screen, a technical phone screen with SQL and systems questions, and onsite rounds covering coding (Python or Java), data modeling and pipeline design, system design for data-intensive applications, and a behavioral interview. The data modeling round is unique to DE roles — you will be asked to design a schema for a specific product use case, considering query patterns, storage efficiency, and downstream consumers. Expect questions about handling late-arriving data, schema evolution, and data lineage tracking.
SQL and Python are the foundation. Among specialized skills, Spark/distributed computing and cloud platform expertise (AWS/GCP) command the highest premiums. dbt and Airflow are increasingly table stakes. Mention specific tools with context: '40+ Airflow DAGs processing 2TB daily'.
Senior DE resumes show: platform architecture decisions, data governance frameworks, cost optimization, mentoring, and cross-team collaboration. Junior resumes focus on pipeline building. Senior bullets start with 'Designed', 'Architected', 'Led' — not 'Built' or 'Wrote'.
Meta is a leading social media and metaverse technology company with a tech stack centered on Hack/PHP, Python, C++, React, GraphQL. Team matching happens AFTER offer. You interview for the company, not a specific team. Move fast and break things philosophy in hiring too. Their culture is move fast. impact-oriented. flat hierarchy. engineers can switch teams every 6 months. strong bootcamp for new hires. For Data Engineer roles, align your resume with these priorities and highlight relevant technologies from their stack.
Meta's typical Data Engineer interview process: Phone screen (1 coding) → onsite (2 coding + 1 system design + 1 behavioral) → team matching. Prepare specifically for Meta's format — their process differs meaningfully from other companies in the industry.
Meta values impact over process — lead every bullet with measurable impact (users affected, revenue generated, latency reduced). Mention experience with large-scale systems serving billions of users. Additionally, Meta's engineering culture emphasizes move fast — weave this into your experience descriptions. Research Meta's recent engineering blog posts and tech talks to reference specific initiatives or technologies they're investing in.
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