Navigating your first 90 days in data engineering

Prepare for your data engineering role by determining how soon before day one you should start and identifying job aspects to prepare in the weeks before you start.

Draft your profile on the worksheet, detailing employment details and expectations, then answer three questions on reasons for accepting the role, learning goals, and things to avoid in data engineering.

Map the data architecture across ingestion, processing, storage, orchestration, and analytics, identify cloud platforms and data services, and prepare before joining to hit the ground running.

Map your data stack visually and plan how to ramp up on unfamiliar tools to avoid overwhelm on day one, focusing on serverless, airflow, and key sql and python skills.

Review the recap and section overview to begin your days in a new role by learning company policies and procedures, and asking the right questions to your manager and peers.

Keep a professional journal to document jargon, tasks, feedback, and impact early in your data engineering journey. Refer to it regularly to avoid confusion and tell your promotion story.

Learn how onboarding training, note-taking on policies and procedures, and the crucial one-on-one with your manager during probation prepare you to meet or exceed expectations.

Learn how to prepare for and conduct a productive one-on-one with your manager, especially during probation, so you meet or exceed expectations and shape your future in the org.

Clarify the purpose behind your role and the team’s function during onboarding, discover who is on the team, and start building rapport through early one-on-ones while taking notes.

Set smart goals with your manager to clarify onboarding expectations, detailing specific tasks, milestones, and a timeline, and verify them by email, including data pipeline for Shopify and dashboard metrics.

Identify your buddy or go-to person for advice during your first 90 days in data engineering, and ask your hiring manager to assign someone whose day-to-day work matches yours.

Request access to the team's applications, including cloud access, databases, GitHub, JIRA, and Confluence, and start this process early. Proactively follow up with access owners to avoid delays.

Learn how the organization tracks goals, KPIs, and milestones to translate work into outcomes, using quarterly OKR or long-term focus, and decide how much and how often to measure.

Meet your buddy to clarify the technical ecosystem, access tools, and align on how data tasks are assigned, balancing ad hoc work with planned projects to prevent burnout.

Onboard by journaling policies and procedures, meeting your manager, and connecting with your team, then advance in data engineering by smart goals, a buddy, and reading code and documentation.

Review the code repo by consulting documentation and data flow diagrams, then verify against the latest code and workflows to map data lineage and transformations.

Check in with repo contributors to confirm alignment, uncover gotchas and roadblocks, learn how approaches were done to identify patterns, and note open items and timelines.

Develop a relational data model to unify multiple sales sources into a single daily net revenue view and plan for sources like TikTok via rest endpoints, with 30-day data retention.

Delve into the data model by applying data lineage, enforcing UTC time zones, and using common constraints and join keys to ensure data quality from origin to destination.

Shadow experienced developers to learn the typical development cycle, environments, version control, IDEs, debugging, testing, and coding style. Absorb how they translate tasks into functional code and document progress.

Gain read-only access to production systems to safely explore the code base and data model. Follow the data pipeline from source extraction through ingestion and processing to the end user.

Review how to read code and documentation through three steps—from a high level view to a deep dive—covering data model concepts and a revenue scenario across point of sale systems.

Learn to map the organization hierarchy, collaborate across departments, and translate data engineering work into business outcomes by aligning with products, industry, target customers, and the business model.

Identify what impact means in your organization by mapping the top three initiatives to key results, measuring revenue and cost effects, and tracking data-infra choices that reduce outages within budget.

Map stakeholders and align data work with organizational goals. Measure success by tracking data infrastructure spending and ROI to reveal the strategic value of data and technology.

Observe and embrace your organization's culture to accelerate progress in your first 90 days. Be present, listen, build relationships, and attend meetings or social gatherings to observe norms.

Learn to read team dynamics, communication styles, and leadership behavior during your first 90 days in data engineering, and align with culture while handling failures and earning recognition.

Explore how to observe and align with organizational culture in your first 90 days, then begin hands-on tinkering to complete your first end-to-end data engineering task.

Days 30 to 60 assess output quality, requiring incremental delivery after learning. At phase end, probation decisions hinge on fit; seek frequent feedback to address overstated skills and core values.

Schedule a 30-day check-in with your manager to obtain specific feedback, clarify performance expectations, and explore growth, promotion, and upskilling opportunities in data engineering.

Choose a simple use case to close critical knowledge gaps and gain hands-on experience in data engineering, seeking reviews and more work in a sandbox environment.

Deliver on modules or subtasks from a larger task to gain exposure and build confidence before delivering an end-to-end solution, iterating and asking in-depth questions in retros and tech discussions.

Leverage hands-on experience and practical application after 30 days by checking in with your manager for feedback. Build confidence by tackling your first end-to-end task in a sandbox with requirements.

Confirm task requirements before coding and map end goals to fields and tables, then seek timely help on critical issues and document assumptions to deliver end-to-end value.

Prioritize attention to detail and rigorous testing, including unit and integration tests with documented outcomes, before production, using pre-prod validation and staged deployment to avoid issues.

Make cheat-sheets early in your development journey by recording fresh observations and potential gaps, flagging anomalies to improve collaboration in data engineering during the first 90 days.

Review end-to-end task tips by confirming requirements, working backwards from the end goal, and balancing collaboration with self-reliance to align with the consumer's perspective and own your tasks.

Navigate your first 90 days in data engineering by completing a 60-day questionnaire, tracking changes toward the 90-day goal, and syncing with your manager on initial goals.

Map your 60-day check-in by evaluating manager feedback, noting tone and adjectives, using journaling for evidence, assert your view calmly, seek guidance to improve core duties and onboarding.

Keep updating your CV with your latest role and tasks, plus the impact you've had, and do so frequently. Track how your wording evolves over time and notice the changes.

Reach the final phase after crossing the 60-day mark by showing autonomy: perform code reviews, pair up for debugging, close tickets, update docs, and drive efficient workflows.

Recap the prior section and stress that handling core responsibilities with minimal supervision, identifying inefficiencies, checking in with your manager, and updating your CV while engaging across departments fosters curiosity.

Carve out time to stay updated by meeting people, talking about your experiences and your work, and boosting visibility and credibility as you advance in your data engineering career.

Stay informed and one step ahead by networking with data professionals, sharing stacks and challenges, and following your company on LinkedIn to track economic and market news.

Participate in hackathons and tech meetups to collaborate, solve problems, and showcase your technical expertise; document your journey by speaking or blogging to build your reputation as a data engineer.

Identify skills gaps in data engineering through continuous learning by tracking trends and speaking with your network. Create a learning plan aligned to your learning style and set smart goals.

Recap the 90-day plan by onboarding, reading data lineage and data model docs, engaging stakeholders, and delivering an end-to-end task, while embracing networking and continuous learning.