1000000882.DATA ENGINEER II.INFO TECH - OPERATIONS

Dallas County•Dallas, TX

3d•$92,589 - $115,736

About The Position

Mid-level technical contributor responsible for designing and maintaining robust data pipelines, performing data transformations, and supporting enterprise reporting and analytics efforts. Works across departments to build scalable solutions that ensure reliable, secure, and high-quality data is available to business users, analysts, and downstream applications. Contributes to data modeling, integration, testing, and documentation in collaboration with analysts, data scientists, developers, and system owners.

Requirements

Education and experience equivalent to a Bachelor’s degree from an accredited college or university in Computer Science, Information Systems, Data Analytics, or in a job-related field of study.
Four (4) years of job-related experience in data engineering, data analytics, or AI/ML data processing.
Must possess a valid Texas Driver’s License and good driving record.
Will be required to provide a copy of 10-year driving history.
Must maintain a good driving record and remain in compliance with Article II, Subdivision II of Chapter 90 of the Dallas County Code.
Individuals holding or considered for a position which has, or may have, access to criminal justice databases including the FBI Criminal Justice Information Systems, NCIC/TCIC and similar databases, must pass a national fingerprint-based records check prior to placement in such position and may be denied placement in such positions and/or access to such systems.
Individuals must also maintain the ability to pass the records check while in the position or until such time that the Commissioners Court and the County Civil Service Commission deem this position no longer has this requirement.
Ability to analyze and solve problems.
Skill in communication, collaboration and documentation.
Ability to work independently, collaboratively on technical projects and mentor junior team members.
Attention to detail and a passion for high-quality, trusted data products.
Ability to design and optimize scalable data workflows.
Skill in SQL, Python, data integration tools (e.g., SSIS, ADF, dbt, Airflow), and/or Scala for data transformation.
Knowledge of cloud platforms (Azure, AWS, or GCP) and data storage technologies (e.g., SQL Server, Snowflake, Parquet, etc.).
Knowledge of Git, CI/CD pipelines, data catalogs, and business intelligence tools.
Knowledge of DevOps, CI/CD, and containerized applications (Docker, Kubernetes).
Knowledge of data privacy, compliance regulations (HIPAA, GDPR, CJIS).
Knowledge of big data frameworks (Hadoop, Spark, Databricks).
Skill in streaming data technologies (Kafka, Kinesis, Pub/Sub).
Knowledge of data warehousing, data lakes, and data modeling best practices.

Nice To Haves

Microsoft Certified: Azure Data Engineer Associate
AWS Certified Data Analytics – Specialty
Snowflake or Databricks certification

Responsibilities

Designs, develops, and maintains scalable data pipelines and workflows across structured and semi-structured data sources.
Writes performant and reusable SQL queries, scripts, or jobs for data ingestion, transformation, and delivery.
Integrates internal and external data sources with enterprise data platforms, lakes, or warehouses.
Performs data profiling, cleansing, and standardization to improve data quality.
Monitors data pipeline health and troubleshoots failures or anomalies.
Documents pipeline architecture, business rules, and data logic for internal users.
Collaborates with DevOps or infrastructure teams to implement automated data processing workflows.
Maintains data access controls, validation rules, and retention policies.
Translates business and analytics requirements into technical data specifications and pipeline designs.
Participates in Agile planning, backlog grooming, and technical design sessions.
Develops data flow diagrams, data models, and transformation logic.
Supports dataset design and delivery for dashboards, reports, or self-service analytics.
Collaborates with application owners to understand source system structures and data changes.
Contributes to solution architecture decisions related to ETL/ELT, storage, and data delivery.
Assists in scoping and estimating new data initiatives and enhancement requests.
Identifies reuse opportunities for data components, tools, or models.
Builds in validation and error-handling logic into data pipelines to support reliability.
Performs root cause analysis for data inconsistencies and recommends preventive actions.
Contributes to and follows testing procedures for data validation, performance, and integrity.
Implements version control, data lineage, and reproducibility practices.
Identifies performance bottlenecks and refactor inefficient data processes.
Recommends improvements to schema design, data granularity, and source-system integration.
Maintains awareness of industry standards for data governance, security, and accessibility.
Supports automation of routine data workflows and manual reporting processes.
Works closely with analysts, data scientists, application developers, and stakeholders to deliver high-quality datasets.
Coordinates with system owners and database administrators to manage source data access and schema changes.
Supports QA and testing teams by validating expected outputs and data quality criteria.
Participates in data design reviews, standups, retrospectives, and sprint demos.
Communicates technical limitations or trade-offs to business stakeholders in an understandable way.
Partners with cybersecurity teams to ensure sensitive data is handled securely and in compliance with County policy.
Engages with BI and reporting teams to ensure datasets meet visual and analytic needs.
Assists in coordinating multi-team efforts involving shared data pipelines or platforms.
Continues building technical proficiency in cloud platforms, big data tools, and data pipeline frameworks.
Pursues professional certifications (e.g., Azure Data Engineer, AWS Data Analytics, dbt, etc.).
Stays current with trends in data engineering, streaming pipelines, and ML Ops practices.
Contributes to internal wikis, playbooks, and best practices documentation.
Mentors junior data engineers or interns on development and testing practices.
Participates in knowledge-sharing sessions, communities of practice, or hackathons.
Seeks opportunities for cross-training with related disciplines (e.g., analytics, DevOps).
Communicates progress, risks, and needs to project leads or data managers.
Documents data sources, logic, and transformations in data dictionaries or metadata repositories.
Support stakeholder training or onboarding on new datasets and data services.
Assists in writing user guides, technical diagrams, and documentation for data pipelines.
Participates in requirement gathering and feedback sessions with business users.
Supports audit and compliance documentation as needed.
Provides timely responses to questions or data requests from supported teams.
Coordinate deployment of data updates with impacted teams or systems.
Performs other duties as assigned.