Data Engineer Job Dallas area,Texas USA,IT/Tech

THIS IS A 5 DAYS A WEEK WORK FROM OFFICE ROLE BASED OUT OF DALLAS/NYC

We are seeking an experienced Data Engineer to design, build, and optimize scalable data pipelines and infrastructure for high-impact analytics and ML workloads.

Responsibilities

Design and implement scalable data pipelines using distributed processing frameworks (Spark, Hadoop, EMR, Map Reduce) to handle large-scale data ingestion, transformation, and delivery.
Develop high-quality data products with strong software engineering practices, leveraging Python, Scala, Java, or equivalent languages.
Architect data infrastructure including storage solutions, data modeling, partitioning strategies, and access patterns optimized for performance and cost.
Build and maintain ETL/ELT workflows that ensure data quality, reliability, and lineage across batch and streaming sources.
Write and optimize complex SQL queries across relational, No

SQL, and data warehouse systems; create views, materialized tables, and performance indexes.
Solve end-to-end data problems
—from source ingestion through transformation, quality validation, and consumption by downstream analytics/ML systems.
Collaborate with data scientists, analysts, and platform teams to define data requirements and SLAs.
Continuously improve pipeline reliability, monitoring, and observability with alerting, logging, and automated testing.
Document data flows, schemas, and lineage for organizational knowledge sharing.

Qualifications

5+ years of professional software engineering experience building data-intensive applications, with deep expertise in Python, Scala, or Java.

3+ years hands-on experience with distributed data processing technologies (Spark, Hadoop, EMR) for building production-scale data pipelines.

Strong expertise in data architecture—modeling (star schemas, dimensional modeling), partitioning, data lakes/warehouses, and infrastructure optimization.

Proven experience building ETL/ELT pipelines from heterogeneous sources (databases, APIs, logs, files) to target systems.

Advanced SQL skills with demonstrated ability to optimize complex queries, create efficient indexes, and handle large datasets.

Track record of owning end-to-end data solutions—identifying root causes, implementing fixes, and measuring business impact.

BS/MS in Computer Science or equivalent practical experience through industry projects.

Strongly

Preferred Experience

Building scalable applications on AWS, GCP, or Azure (S3, Redshift, Big Query, Glue, Lambda, etc.).
Streaming data pipelines using Spark Streaming, Flink, Kafka, or Kinesis.
Hands-on experience with Databricks and/or Snowflake for unified analytics.
Data quality frameworks, schema evolution, and data governance practices.
Experience with orchestration tools (Airflow, Prefect, Step Functions) and CI/CD for data pipelines.

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