Data engineering has rapidly become one of the most in-demand career paths in technology. As enterprises modernize their platforms and embrace cloud and AI, the role of the data engineer has expanded well beyond writing pipelines. Today's data engineers are expected to be architects, integrators, and guardians of data quality, helping organizations turn raw information into trusted, actionable insights.
For new graduates entering this field, the opportunities are significant—but so is the competition. At KData, we work every day with enterprises and staffing partners to deploy certified data engineering talent on critical projects. We see firsthand the skills that make candidates stand out, and which capabilities employers value most. Whether you're looking to secure your first role or accelerate your growth, here are the best technical skills to focus on as you build your career in data engineering.
1 SQL: The Language of Data
No matter how advanced the tools become, SQL remains the backbone of data engineering. Employers expect fluency in SQL as a given. This goes beyond writing simple queries: it's about understanding how to join large datasets, optimize performance, use window functions, and design queries that scale.
A new graduate who can confidently demonstrate SQL proficiency signals immediate value to hiring managers. Whether the environment is Databricks, Snowflake, BigQuery, or a traditional data warehouse, SQL remains the universal skill.
What to focus on:
- Mastering analytical functions (ROW_NUMBER, RANK, LAG/LEAD).
- Writing optimized queries for big data environments.
- Designing schemas and understanding normalization/denormalization trade-offs.
2 Python: The Glue Language of Data Engineering
If SQL is the foundation, Python is the glue that binds modern data systems. Python is used for building ETL/ELT pipelines, orchestrating workflows, and even applying machine learning in data engineering contexts. For new grads, strong Python skills are a must.
The key is not to become a software engineer, but to focus on how Python is applied in data engineering: libraries like Pandas for data manipulation, PySpark for distributed processing, and automation scripts for repetitive tasks.
What to focus on:
- Building reusable ETL scripts.
- Using PySpark to scale processing on large datasets.
- Automating validations and transformations.
3 Databricks and the Spark Ecosystem
With its rise as the de facto platform for modern data and AI, Databricks skills are career-accelerating. Many enterprises are investing heavily in Databricks to unify their data lakes and warehouses, and they need talent who can deliver quickly.
For a new grad, achieving the Databricks Certified Data Engineer Associate credential is an excellent way to demonstrate readiness. At KData, we view this as the baseline certification for many of our placements. The Professional level is even more valuable.
Beyond certification, employers want to see familiarity with Databricks' ecosystem:
- Delta Lake & Delta Live Tables (DLT) for building reliable pipelines.
- Unity Catalog for data governance and lineage.
- MLflow for tracking models in data-centric workflows.
- Workflows and Notebooks for orchestration and collaboration.
These skills show that a graduate can operate in environments where speed, scale, and governance all matter.