Data Engineer

At Accenture Federal Services, nothing matters more than helping the US federal government make the nation stronger and safer and life better for people. Our 13,000+ people are united in a shared purpose to pursue the limitless potential of technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations. Join Accenture Federal Services, a technology company within global Accenture. Recognized as a Glassdoor Top 100 Best Place to Work, we offer a collaborative and caring community where you feel like you belong and are empowered to grow, learn and thrive through hands-on experience, certifications, industry training and more. Join us to drive positive, lasting change that moves missions and the government forward! ob Description: Data Engineer We are looking for a skilled and passionate Data Engineer to join our team. You will play a critical role in designing, building, and maintaining our data infrastructure to ensure seamless data flow, scalability, and reliability. You will work closely with data scientists, analysts, and other stakeholders to develop efficient data pipelines, manage large datasets, and integrate machine learning models into production environments. Key Responsibilities: Programming Fundamentals : Write clean, efficient, and scalable code to build and optimize data solutions using programming languages like Python. Data Pipeline Development : Design, build, and orchestrate robust and reliable data workflows using tools such as Apache Airflow, dbt, Prefect, or Dagster. Cloud Platform Familiarity : Work comfortably in cloud environments, with a strong preference for experience in AWS. Experience in GCP or Azure is also highly valued. Database Querying Skills : Extract, integrate, and ensure the quality of data from various sources using tools and technologies such as SQL, PostgreSQL, Snowflake, Amazon Redshift, or BigQuery. Big Data Processing : Leverage frameworks like Apache Spark, Databricks, or Apache Kafka to process and manage large-scale data workflows with reliability and efficiency. ML Integration / MLOps : Support the implementation, deployment, and scaling of machine learning models in production environments using tools like Amazon SageMaker, MLflow, or Kubeflow. Monitoring Troubleshooting : Monitor data pipeline health, troubleshoot issues, and ensure data consistency using tools such as Amazon CloudWatch, Datadog, or Great Expectations. Collaboration Documentation : Work closely with data scientists, analysts, and other stakeholders to understand data requirements, communicate solutions, and document processes using tools like Git, Jira, and Confluence. Qualifications: 2 years of experience as a Data Engineer or similar role. Strong proficiency in Python or other programming languages relevant to data engineering. Any additional experience with any of the following: Hands-on experience with data pipeline orchestration tools (e.g., Apache Airflow, dbt, Prefect, Dagster). Solid understanding of cloud platforms (AWS strongly preferred; GCP or Azure experience also considered). Expertise in SQL and familiarity with relational and columnar databases (e.g., PostgreSQL, Snowflake, BigQuery). Knowledge of big data processing frameworks (e.g., Apache Spark, Databricks, or Apache Kafka). Familiarity with machine learning workflows and experience implementing MLOps tools (e.g., Amazon SageMaker, MLflow, or Kubeflow) in production environments. Strong troubleshooting skills and experience monitoring data pipelines and system health using tools like Amazon CloudWatch, Datadog, or Great Expectations. Excellent communication skills and a collaborative mindset, with a focus on documentation and best practices. Preferred Skills: Experience working with large-scale distributed systems. Knowledge of data governance and security best practices. Proven ability to work in cross-functional teams and contribute to problem-s ... (truncated, view full listing at source)